{"as_of":"2026-08-13T15:07:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:93f4bc8282d93644112d956a92b5269d1fc2036d21e15937d9d27c264e86e079","coverage":[{"denominator":250,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T15:17:06.858095Z","state":"measured"},{"denominator":104,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":104,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T09:03:31.868534Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-06-30T08:14:25.864392Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2512.17534","snapshot_observed_at":"2026-08-03T09:03:31.868534Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2601.15015","last_updated":"2026-05-27T13:43:47Z","snapshot_observed_at":"2026-08-07T04:34:33.616763Z","submitted_at":"2026-01-21T14:13:44Z","title":"Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Control","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-03T09:03:31.868534Z"},"links":{"cited_paper":"/paper/2512.17534","citing_paper":"/paper/2601.15015"},"observation_digest":"sha256:d28e81b783bb195d0b597761b58b860d933f6caaa1ae588251b99a800e6b1fe9","observation_id":"8f7ac81b-ebaa-4290-8b52-27b4cb57f1e0","resolution":{"observed_at":"2026-08-03T09:03:31.868534Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"cited_work":{"arxiv_id":"2512.17534","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2512.17534","snapshot_observed_at":"2026-07-01T02:17:18.302189Z","title":"Lagemann,et al., HydroGym: a reinforcement learning platform for fluid dynamics, arXiv:2512.17534 (2025)","venue":null,"work_id":"7980792d-f5c5-4ceb-85fb-ef6a3061706d","year":2025},"citing_paper":{"arxiv_id":"2604.09434","last_updated":"2026-04-13T02:40:35Z","snapshot_observed_at":"2026-07-06T22:58:17.167367Z","submitted_at":"2026-04-10T15:50:44Z","title":"Physics-guided surrogate learning enables zero-shot control of turbulent wings","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-10T17:00:02.552359Z"},"links":{"cited_paper":"/paper/2512.17534","citing_paper":"/paper/2604.09434"},"observation_digest":"sha256:e48d3bf1fb9f2d4849a62f12729cf50b86a1bc181f3624b0ebb80a54e1e05b12","observation_id":"935d9859-40d7-41e3-b6c0-bc88eefca31e","resolution":{"observed_at":"2026-07-01T02:17:18.302189Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"cited_work":{"arxiv_id":"2512.17534","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2512.17534","snapshot_observed_at":"2026-07-01T02:17:18.302189Z","title":"Lagemann,et al., HydroGym: a reinforcement learning platform for fluid dynamics, arXiv:2512.17534 (2025)","venue":null,"work_id":"7980792d-f5c5-4ceb-85fb-ef6a3061706d","year":2025},"citing_paper":{"arxiv_id":"2606.29047","last_updated":"2026-06-27T19:05:50Z","snapshot_observed_at":"2026-07-31T18:51:56.319575Z","submitted_at":"2026-06-27T19:05:50Z","title":"Weak Dominant Balance for Robust Identification of Dynamically Consistent Fluid Flow Structure","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-30T08:05:41.876889Z"},"links":{"cited_paper":"/paper/2512.17534","citing_paper":"/paper/2606.29047"},"observation_digest":"sha256:eebe3789f65e07d0600db472810e68d71f27d7ea913b68baf62d5f1a053e69f1","observation_id":"33ce9bce-6cbe-4356-aa86-d8ab984c9e8f","resolution":{"observed_at":"2026-07-01T02:17:18.302189Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2512.17534","snapshot_observed_at":"2026-07-12T05:51:52.787362Z","title":"arXiv preprint arXiv:2512.17534 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.02954","last_updated":"2026-07-03T04:58:41Z","snapshot_observed_at":"2026-08-06T10:25:27.361839Z","submitted_at":"2026-07-03T04:58:41Z","title":"Microcosmos: Reimagining Artificial Life for the GPU Era","version":1},"reference_index":82,"source":"arxiv_source","source_observed_at":"2026-07-12T05:51:52.787362Z"},"links":{"cited_paper":"/paper/2512.17534","citing_paper":"/paper/2607.02954"},"observation_digest":"sha256:4e140a78fa1498efd7372c9ea92da5e96f84676536d64c3f2e957ba0bc74baca","observation_id":"c79c3f35-949f-4952-8b05-938edc226cfd","resolution":{"observed_at":"2026-07-12T05:51:52.787362Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2512.17534/citation-record","integrity":"/paper/2512.17534/integrity","json":"/paper/2512.17534/citation-record.json","paper":"/paper/2512.17534"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:16:56.522320Z","title":"Marusic, D","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-03T15:16:56.522320Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:27d40d34c479b39508ef870c25b476bcb5f2c29fb9cc7f2148daa59ad378402b","observation_id":"9fecdfe6-b993-4522-a8c0-d5fbc3e851c4","resolution":{"observed_at":"2026-08-03T15:16:56.522320Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:16:56.765882Z","title":"Mäteling, M","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-03T15:16:56.765882Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:bec96082f671885cfcdf77337b0e8d5ac2e4f2c4f3b06c893938bd05024a70ea","observation_id":"f2db149f-8416-4a56-b8a1-e7eaf3b916fe","resolution":{"observed_at":"2026-08-03T15:16:56.765882Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:16:56.847838Z","title":"A review of turbulent skin-friction drag reduction by near-wall transverse forcing.Prog","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-03T15:16:56.847838Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:3fb762e026cbfa791d8261a2e073aebba9e340da70a1dba0fe1faff9f20f3a30","observation_id":"fc7fdb3c-9712-4b57-a5a8-9b770c84c0ad","resolution":{"observed_at":"2026-08-03T15:16:56.847838Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:16:56.959709Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-03T15:16:56.959709Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:433abc70b284bf14a95f44f6b83225cede8054a44cca9dfbf361524a85360a74","observation_id":"441e889c-352e-4fdf-8de9-3d7311b13cbf","resolution":{"observed_at":"2026-08-03T15:16:56.959709Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:16:57.040307Z","title":"Cooperative wind farm control with deep reinforcement learning and knowledge-assisted learning.IEEE Transactions on Industrial Informatics, 16(11):6912–6921, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-03T15:16:57.040307Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:c4fc805b4bc3db8188c19a7f01220f8b6871afa10ab93a1fc4c835918b839fc0","observation_id":"39bf67f9-125f-4026-ad14-9a3b1d106cf8","resolution":{"observed_at":"2026-08-03T15:16:57.040307Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:16:57.137497Z","title":"Dalili, A","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-03T15:16:57.137497Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:4a1dd07ddc1201df93128e6fd91472a20b77a75510aee248d06291e0e05521f7","observation_id":"b6f43300-2797-4597-b22c-84698504aee5","resolution":{"observed_at":"2026-08-03T15:16:57.137497Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:16:57.188369Z","title":"P Chamorro, REA Arndt, and Fotis Sotiropou- los","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-03T15:16:57.188369Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:4ff63a124c10bd49182d5ed427c28eab11091c3ef48268e85fac1f38cee00b94","observation_id":"52ed99ea-731a-4d16-a6f7-45c59cd50618","resolution":{"observed_at":"2026-08-03T15:16:57.188369Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:16:57.255090Z","title":"Kaltenbach, and P","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-03T15:16:57.255090Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:6233345d7b9a02efd1c0dfa9e9787064639d67868a783b275c2a39f866242948","observation_id":"c2b2d60f-dac7-4f83-aee0-e3e5520a7d34","resolution":{"observed_at":"2026-08-03T15:16:57.255090Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:16:57.311942Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-03T15:16:57.311942Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:f714fed0b0768997ab6c08d2f92145c5dcaa768ff839a63a3fe39731678f2be0","observation_id":"5e556ed3-baf7-40c5-a03b-10f210e0682f","resolution":{"observed_at":"2026-08-03T15:16:57.311942Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:16:57.361752Z","title":"Brunton, B","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-03T15:16:57.361752Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:a81ae72845d3dfa00e8a6bfa32dddfd8b7bc2455b9c2ea1cfa1ba0787e753807","observation_id":"b959e630-1015-491d-a31e-18cef3c713ca","resolution":{"observed_at":"2026-08-03T15:16:57.361752Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:16:57.432739Z","title":"Koumoutsakos","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-03T15:16:57.432739Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:3b2fb4140984e2f876acda1ee4d9cea69a3b3ff34475546b4366aea708dec5ca","observation_id":"6fb95bc3-f77e-4858-8a5d-6d686e30f709","resolution":{"observed_at":"2026-08-03T15:16:57.432739Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:16:57.486785Z","title":"Jumper, R","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-03T15:16:57.486785Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:6474521cfccf15388551c221a288eaf5c4060683f661f57c2a1d49e5c871e115","observation_id":"51a2bd65-65b6-4238-a48d-685f30d5eff1","resolution":{"observed_at":"2026-08-03T15:16:57.486785Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:16:57.603050Z","title":"Degrave, F","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-03T15:16:57.603050Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:f0f5b2efd2c01fb7a0fbfc58aca9fb73e2bc8774ba58733b1b8017195197e626","observation_id":"f6023e04-c30d-40e5-954f-ef8ff0c818c2","resolution":{"observed_at":"2026-08-03T15:16:57.603050Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:16:57.724653Z","title":"Kochkov, J","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-03T15:16:57.724653Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:585b89944ee42d60db2cb914e27661a4f977ebad7ad50688017a7c4239fdea8c","observation_id":"101d6308-f201-450c-b25d-a2b17a256899","resolution":{"observed_at":"2026-08-03T15:16:57.724653Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:16:57.828912Z","title":"Rabault, Arnau Miró, Bernat Font, O","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-03T15:16:57.828912Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:f99d5550a66a309d604d3ea3c410088f2b2d3631f52c30d7fedfe66f9bffca1e","observation_id":"e2477764-74bb-4801-8bd5-e07364f0940d","resolution":{"observed_at":"2026-08-03T15:16:57.828912Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:16:57.895899Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-03T15:16:57.895899Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:eb171b398b0f564df2a72531ca99ab41e44c268184aedfbb3cab317a6936bca6","observation_id":"bb66d372-4efc-4a49-b50c-fa6b48af3c56","resolution":{"observed_at":"2026-08-03T15:16:57.895899Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.09110","last_updated":"2025-08-22T20:31:51Z","snapshot_observed_at":"2026-08-13T00:54:37.320358Z","submitted_at":"2024-03-14T05:17:39Z","title":"SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.09110","snapshot_observed_at":"2026-08-03T15:16:58.053801Z","title":"Zolman, C","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-03T15:16:58.053801Z"},"links":{"cited_paper":"/paper/2403.09110","citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:cba7949d4ad0d954a66b2b85c71a33ef5a9ea0328507a0410cf5d0a264618b17","observation_id":"c822769a-c5ac-48f8-b26d-6239bb6c242e","resolution":{"observed_at":"2026-08-03T15:16:58.053801Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:16:58.184396Z","title":"Vinyals, I","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-03T15:16:58.184396Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:33284461b01bd92ef026cf7f504619161fedccbba5047d9160c2afd2fe967440","observation_id":"3314fc7a-d570-4d2e-9e1b-eebb14029aa3","resolution":{"observed_at":"2026-08-03T15:16:58.184396Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:16:58.276274Z","title":"Chatzimanolakis, P","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-03T15:16:58.276274Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:8794e0c0b004041403b0d3f8b05e097b58cac9e43d003ed1029707bfb804f8dd","observation_id":"a396ce9e-2687-4efe-bc13-e07124d0024b","resolution":{"observed_at":"2026-08-03T15:16:58.276274Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:16:58.361128Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-03T15:16:58.361128Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:e79cc9fc1168ec2a0f616d69878113ef808cbe790ab4f0ac872473133cc088c5","observation_id":"8a716013-2310-47f7-97e8-319dcd0e7f14","resolution":{"observed_at":"2026-08-03T15:16:58.361128Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:16:58.446468Z","title":"Bhola, S","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-03T15:16:58.446468Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:1a6c9b51166aaf5a13253905c6663951f2d41531b028c26c78d6b14440a2708b","observation_id":"1d377ba4-fecb-400a-a077-6cc876fe0dc1","resolution":{"observed_at":"2026-08-03T15:16:58.446468Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:16:58.524272Z","title":"Imagenet: A large-scale hierarchical image database","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-03T15:16:58.524272Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:88e2492a730ece3fcc01b075bec9ce1a3d7dd1d15b43e798a059269befad506d","observation_id":"1b89b4d9-8783-4dea-8b5c-a48c6d5f06a2","resolution":{"observed_at":"2026-08-03T15:16:58.524272Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:16:58.627206Z","title":"Todorov, T","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-03T15:16:58.627206Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:530d53401e82c0680035a2ea84ec552e676d3a35a4b3052b7bc5a6761cd15cb1","observation_id":"6fe4d59a-65dd-44a2-973e-34c53b6f757d","resolution":{"observed_at":"2026-08-03T15:16:58.627206Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.5281/zenodo.13350586","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"m-AIA, 2024","venue":"Zenodo (CERN European Organization for Nuclear Research)","work_id":"dccafde4-a250-4e60-ad04-9843da0fa998","year":2024},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-03T15:16:58.686071Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:1e7393b039664f3a2632caf4e71536ed8cc9a22c88abc1028d8d4975a6d1229e","observation_id":"aefb4420-477e-4ddd-a10c-ec22b39492d2","resolution":{"observed_at":"2026-08-03T15:19:17.192040Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:16:58.743094Z","title":"Bradbury, R","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-03T15:16:58.743094Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:325e0fa8acd14ba660f1234a755493f0a296e6a2cb0a6f7a3576ffd6cd723b92","observation_id":"a0a86c01-ffd7-4886-9103-fdee7f16558e","resolution":{"observed_at":"2026-08-03T15:16:58.743094Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:16:58.812370Z","title":"Ham, Paul H","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-03T15:16:58.812370Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:d762ac15de68ff0604a93c893e509edb8c1a86c4c3f434836c773467080eef9d","observation_id":"6b8bf0e2-d53e-4965-9972-9ad4f107ef88","resolution":{"observed_at":"2026-08-03T15:16:58.812370Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.17032","last_updated":"2025-11-02T13:42:19Z","snapshot_observed_at":"2026-08-12T13:10:57.260306Z","submitted_at":"2024-07-24T06:35:05Z","title":"Gymnasium: A Standard Interface for Reinforcement Learning Environments","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.17032","snapshot_observed_at":"2026-08-03T15:16:58.894784Z","title":"Gymnasium: A standard inter- face for reinforcement learning environments.arXiv preprint arXiv:2407.17032, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-03T15:16:58.894784Z"},"links":{"cited_paper":"/paper/2407.17032","citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:dedbd0d0adf0dffeea896e680a2ac2daad500c5e61d1c4bbc628346f8f4a4958","observation_id":"d9f81cab-8180-4215-9a49-47f63f87206c","resolution":{"observed_at":"2026-08-03T15:16:58.894784Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:16:58.974778Z","title":"Drake: Model-based design and verification for robotics, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-03T15:16:58.974778Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:74c0d0e0e0011dcaef8144445a678f065e4bc28b0de9054840e3a01b5c7166dd","observation_id":"bdffce43-e758-4134-801b-10777291052e","resolution":{"observed_at":"2026-08-03T15:16:58.974778Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2108.10470","last_updated":"2021-08-25T23:42:59Z","snapshot_observed_at":"2026-07-06T11:40:56.544714Z","submitted_at":"2021-08-24T01:38:11Z","title":"Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.10470","snapshot_observed_at":"2026-08-03T15:16:59.032521Z","title":"Macklin, D","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-03T15:16:59.032521Z"},"links":{"cited_paper":"/paper/2108.10470","citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:7ffd287b8ed3c1a032d76b7d73c36526823fa195d1787b23b00ec10306fa1291","observation_id":"15ec320e-410a-4ffb-ab3a-a99b418fbbd0","resolution":{"observed_at":"2026-08-03T15:16:59.032521Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:16:59.167771Z","title":"Mittal, C","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-03T15:16:59.167771Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:eff7b870ea1ce0037ebc96251b9465dc6f2cb2979ae37c2effc43a9c95cd4c0d","observation_id":"a822b1aa-32b9-4406-8ca6-5f2c0a887249","resolution":{"observed_at":"2026-08-03T15:16:59.167771Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:16:59.371334Z","title":"Suárez, F","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-03T15:16:59.371334Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:c6100d5a89eff3b2dca73ea734cba6822bd62eede0a35b237603484c3e5e8b84","observation_id":"753b59ae-6f5a-4601-958f-97343b620feb","resolution":{"observed_at":"2026-08-03T15:16:59.371334Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1707.06347","last_updated":"2017-08-28T09:20:06Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-07-20T02:32:33Z","title":"Proximal Policy Optimization Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.06347","snapshot_observed_at":"2026-08-03T15:16:59.467186Z","title":"Schulman, F","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-03T15:16:59.467186Z"},"links":{"cited_paper":"/paper/1707.06347","citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:1868bddc13763e719320409c673a2f1da92a1b08ce2d328ccda3a5772dd9ce54","observation_id":"863b0f91-a334-4a81-af97-e2f7bc7e7fc7","resolution":{"observed_at":"2026-08-03T15:16:59.467186Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1509.02971","last_updated":"2019-07-05T10:47:27Z","snapshot_observed_at":"2026-07-06T04:29:24.362640Z","submitted_at":"2015-09-09T23:01:36Z","title":"Continuous control with deep reinforcement learning","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1509.02971","snapshot_observed_at":"2026-08-03T15:16:59.607304Z","title":"P Lillicrap, J","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-03T15:16:59.607304Z"},"links":{"cited_paper":"/paper/1509.02971","citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:2ec9a5528779b5e9ad5bc74dca686173317a68257598ce173192db7bb9130c69","observation_id":"99141337-069f-459a-a7a5-5b497099fe8a","resolution":{"observed_at":"2026-08-03T15:16:59.607304Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:16:59.733312Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-03T15:16:59.733312Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:e08366a423436788cfc3f93ed4ef8a81f234d367e6f886b43cf26006b82aaa29","observation_id":"001cc3c5-da62-4873-8b6d-bec6c02ed250","resolution":{"observed_at":"2026-08-03T15:16:59.733312Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:16:59.796794Z","title":"Dittert, Vikash Kumar, Shagun Sodhani, Xiaomeng Yang, Gianni De Fab- ritiis, and Vincent Moens","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-03T15:16:59.796794Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:4a0a92fbcd9c7c28498ec4c38e868c06f688fb85a08d422075c877b233b25d1a","observation_id":"20d9dc24-e7c9-4eca-99d6-ec7b0a7178e1","resolution":{"observed_at":"2026-08-03T15:16:59.796794Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:16:59.910303Z","title":"Huang, R","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-03T15:16:59.910303Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:b738ff6d601e13828093242f3430c9c56783dd0b43ccde21dcb480baa70e54a8","observation_id":"d4b3f6a2-b72a-4b84-bd2e-260c065460fd","resolution":{"observed_at":"2026-08-03T15:16:59.910303Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:16:59.969148Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-03T15:16:59.969148Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:3f1218b7ffab10cbf0c7a0a872b9ed2bf8e9a485999e7f902b15b15869dc8ca9","observation_id":"38962a61-fe54-4c65-ada6-646f8f4ad221","resolution":{"observed_at":"2026-08-03T15:16:59.969148Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2204.07137","last_updated":"2022-04-14T17:46:26Z","snapshot_observed_at":"2026-07-06T13:00:30.959591Z","submitted_at":"2022-04-14T17:46:26Z","title":"Accelerated Policy Learning with Parallel Differentiable Simulation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2204.07137","snapshot_observed_at":"2026-08-03T15:17:00.005632Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:00.005632Z"},"links":{"cited_paper":"/paper/2204.07137","citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:79bf9b9bb9ed7a291aaef3e997d52bd4ba0ae27bf0e3e3541cf001d80ad30617","observation_id":"dd766bdc-d84c-4c22-a16e-d27478321498","resolution":{"observed_at":"2026-08-03T15:17:00.005632Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:00.065011Z","title":"Direct numerical simulation of turbulent channel flow up to re= 590.Phys","venue":null,"work_id":null,"year":1999},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:00.065011Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:6f2640585d2d4d22c8f72fd3d493be1903e76e3b7843c68433a7984dbd63bcbf","observation_id":"04a3b738-f989-4644-a2a6-e9c733e74aba","resolution":{"observed_at":"2026-08-03T15:17:00.065011Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:00.128191Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:00.128191Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:05b0b9680af1784a838d4bbd183c1e6c20c9d424e22af66c30f09667875e9f92","observation_id":"794b016c-20d0-41cc-b1e5-cbe676003759","resolution":{"observed_at":"2026-08-03T15:17:00.128191Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:00.175550Z","title":"Blanchard and T","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:00.175550Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:362662917506d1dee6bb4f59ac727f2c9f266ad35a340f007a171470cd61796f","observation_id":"19560847-3f67-4b69-b878-e983b7fdfce7","resolution":{"observed_at":"2026-08-03T15:17:00.175550Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:00.243181Z","title":"W Rowley, D","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:00.243181Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:2609a0b9ece111fdfb65766f6d550af0ed1784cff38570e59b05aab491e0188f","observation_id":"11cf997c-8c0a-4400-be3a-317f550dae9f","resolution":{"observed_at":"2026-08-03T15:17:00.243181Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:00.330742Z","title":"R Williams, C","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:00.330742Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:ac95fa182c1f4ebf6f43eafc4c58e402674d875599bdc94e1f2694a0d7e2ec4b","observation_id":"ce65ba33-c85f-4720-8e43-22073743460b","resolution":{"observed_at":"2026-08-03T15:17:00.330742Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:00.399908Z","title":"Bagheri, J","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:00.399908Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:6165e7b6a3db53f80eed2392b7f694901ed44a5a1a40a91cb0a3168df5a00050","observation_id":"132863cf-75fd-4a5a-aeff-5f7185d041e6","resolution":{"observed_at":"2026-08-03T15:17:00.399908Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:00.439259Z","title":"Colonius and D","venue":null,"work_id":null,"year":1940},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:00.439259Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:f112a0f9e7893f28b1399ad11d4d627a7c740b5422f4b5c7f2f9c8daf4b5b8c8","observation_id":"2789ef54-d376-4bce-93d9-1a1df25eca3a","resolution":{"observed_at":"2026-08-03T15:17:00.439259Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:00.513285Z","title":"Sipp and P","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:00.513285Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:c7b4f03e3f240b8040e44730062236f04c2cdb11032eeb7f29c05b9348b9bb66","observation_id":"6fdd5606-131f-40a8-b7df-69ae07f548be","resolution":{"observed_at":"2026-08-03T15:17:00.513285Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:00.564283Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:00.564283Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:7289c26e910a7c9f0d8d4a66a256f6e6a743ca3e1258aac26af4844dc7cdc426","observation_id":"081ed172-94cc-45b0-a2a4-ebd20b02f26c","resolution":{"observed_at":"2026-08-03T15:17:00.564283Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:00.625566Z","title":"List, L.-W","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:00.625566Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:df4d26642289b148292635dad1f9288675a59030887594f934dbd039c10a22db","observation_id":"54510943-3660-470a-83f2-48b4f13b44b9","resolution":{"observed_at":"2026-08-03T15:17:00.625566Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:00.673176Z","title":"Koehler, S","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:00.673176Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:5fd38e640a4f76644ed1b895c1fd692a49c54aba2bc1ffd5734a202b9666abac","observation_id":"472c727e-8af7-46fb-adf1-943dc7dfcc57","resolution":{"observed_at":"2026-08-03T15:17:00.673176Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.07457","last_updated":"2020-01-21T11:58:41Z","snapshot_observed_at":"2026-08-13T04:37:43.329872Z","submitted_at":"2020-01-21T11:58:41Z","title":"Learning to Control PDEs with Differentiable Physics","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.07457","snapshot_observed_at":"2026-08-03T15:17:00.757816Z","title":"Learning to control pdes with differentiable physics","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:00.757816Z"},"links":{"cited_paper":"/paper/2001.07457","citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:6782ae3b489d7e1913be2631e106d6e783f496e2ceae8cd5d3b73796214670b1","observation_id":"75663103-d56a-4bad-bca4-aa2f388e4937","resolution":{"observed_at":"2026-08-03T15:17:00.757816Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:00.911804Z","title":"Maulik, K","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:00.911804Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:c7d948de84dc4007a69e9e4e4cd0683132682c94b7aa52e185195896b757d238","observation_id":"ad5f5115-7a56-43ae-8652-35727f71df82","resolution":{"observed_at":"2026-08-03T15:17:00.911804Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:01.079206Z","title":"Duraisamy, G","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:01.079206Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:836fdf8920a4ac02aee61340548d63e2d9c28a3dd9ed341e45237be959ddb747","observation_id":"52572c35-47bc-4465-937c-2fdf28297ee0","resolution":{"observed_at":"2026-08-03T15:17:01.079206Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:01.245652Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:01.245652Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:849407107a7a860eda968f306ab50fc6158ce6b281144f68d3e125aafdb9a621","observation_id":"8ad57d4e-6e2f-4311-b69f-83dffa8a3ac0","resolution":{"observed_at":"2026-08-03T15:17:01.245652Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:01.438932Z","title":"Brandt, and Dan S Henningson","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:01.438932Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:1f134967253d3246d9f97fb82617ff323110cbcce0661a9b55d08848c1c3c505","observation_id":"4a4a7323-669b-4428-b717-dd37cf8b4010","resolution":{"observed_at":"2026-08-03T15:17:01.438932Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.02354","last_updated":"2026-05-22T17:37:19Z","snapshot_observed_at":"2026-08-02T15:40:15.186438Z","submitted_at":"2025-04-03T07:42:47Z","title":"Improving turbulence control through explainable deep learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.02354","snapshot_observed_at":"2026-08-03T15:17:01.572229Z","title":"Improving turbulence control through explainable deep learning.arXiv preprint arXiv:2504.02354, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:01.572229Z"},"links":{"cited_paper":"/paper/2504.02354","citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:d3fef096e06cbed736bbd3a8a8495c98b3d5bdd7745296b3d0586cd2af9a7095","observation_id":"393c59d6-7653-4f53-b001-1d9d66b6d598","resolution":{"observed_at":"2026-08-03T15:17:01.572229Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:01.767239Z","title":"Cremades, S","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:01.767239Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:a29b63fb8903c244aa63dd9bee5ef3faa30576ee0200a6402d2b793a39efbf63","observation_id":"86bcf5d9-f6bf-4b6f-8084-df8314967d75","resolution":{"observed_at":"2026-08-03T15:17:01.767239Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:01.901755Z","title":"Lozano-Durán and H","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:01.901755Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:c6921af0ed9480b4f5b0692e89eee0f911137cefcf865874c447eb27fc1fe53d","observation_id":"db64bed6-9d34-4700-8e45-0875148d403c","resolution":{"observed_at":"2026-08-03T15:17:01.901755Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:02.052104Z","title":"Martínez-Sánchez, G","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:02.052104Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:3d15b77f4cb288abd2723a86baa4416f629110f2e8849c6890e2bdf29af72e50","observation_id":"25c1032c-b2c8-48b2-8b94-4b83668fd409","resolution":{"observed_at":"2026-08-03T15:17:02.052104Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:02.194757Z","title":"Lagemann, B","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:02.194757Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:d72d828679c5f3cd5e3428cf86f78d8a0f1ff61ca32f3520cfc7dff996d4ba9b","observation_id":"0a0e9022-2fed-4357-9c74-08e36dba262d","resolution":{"observed_at":"2026-08-03T15:17:02.194757Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:02.288017Z","title":"Cranmer, Alvaro Sanchez-Gonzalez, Rui Battaglia, P .and Xu, Kyle Cranmer, D","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:02.288017Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:d5b16c6712c06865ac4afe8bee0588896d85133419294525eaaf3cde01ba7c57","observation_id":"bf53f227-6ad3-44e3-a294-1e3c8a663c86","resolution":{"observed_at":"2026-08-03T15:17:02.288017Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:02.407128Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:02.407128Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:8edd0773fd2dd076ca92b1efbf46360c0f421e2ed692da45a3c515dc414de96a","observation_id":"b8c0ab14-e768-4404-96ce-eea3a7609b28","resolution":{"observed_at":"2026-08-03T15:17:02.407128Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:02.538388Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:02.538388Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:87d29f62daf142875d176be5a56ee5dba594baa69028c3dab9e9c2cf8cdfa94d","observation_id":"45301218-00c1-4c68-bc27-5280b0543f85","resolution":{"observed_at":"2026-08-03T15:17:02.538388Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1606.01540","last_updated":"2016-06-05T17:54:48Z","snapshot_observed_at":"2026-08-13T12:26:05.192883Z","submitted_at":"2016-06-05T17:54:48Z","title":"OpenAI Gym","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1606.01540","snapshot_observed_at":"2026-08-03T15:17:02.611884Z","title":"Openai gym.arXiv preprint arXiv:1606.01540, 2016","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:02.611884Z"},"links":{"cited_paper":"/paper/1606.01540","citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:8b3a73f96b73a7b077b2e8a86a5a8c9cd975821e5be8617f94c28b848ae8d005","observation_id":"354b5364-fb16-420b-bec1-406db5994b2d","resolution":{"observed_at":"2026-08-03T15:17:02.611884Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:02.696288Z","title":"Vinuesa and S","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:02.696288Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:c21fdf71d42f819ae2a358a69abf2f6f03e6901289741e556c890edcf67b6610","observation_id":"1520d19d-776d-4419-9f3e-b4bb6536fc87","resolution":{"observed_at":"2026-08-03T15:17:02.696288Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.12089","last_updated":"2025-02-27T19:05:47Z","snapshot_observed_at":"2026-08-13T04:39:12.100916Z","submitted_at":"2024-12-16T18:56:24Z","title":"Stabilizing Reinforcement Learning in Differentiable Multiphysics Simulation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.12089","snapshot_observed_at":"2026-08-03T15:17:02.750342Z","title":"Stabilizing re- inforcement learning in differentiable multiphysics simulation.arXiv preprint arXiv:2412.12089, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:02.750342Z"},"links":{"cited_paper":"/paper/2412.12089","citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:6a5d86828f1b487079ec5b51ed37ffbd93baba2b8b405cbcea2de6c8ad044343","observation_id":"d80812c7-5ba6-474f-8198-3150962b2a74","resolution":{"observed_at":"2026-08-03T15:17:02.750342Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:02.831936Z","title":"On the choice of physical constraints in arti- ficial neural networks for predicting flow fields.Fu- ture Generation Computer Systems, 161:361–375, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:02.831936Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:05ee0738376a8fff434362b6b822045b03d223669553427809ded985638aff6e","observation_id":"4b80c33d-e0e6-4b46-b8d7-4de5d4b3f1ed","resolution":{"observed_at":"2026-08-03T15:17:02.831936Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:02.918176Z","title":"Large-eddy simulation of turbulent flow over the drivaer fastback vehicle model.Journal of Wind En- gineering and Industrial Aerodynamics, 186:123–138, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:02.918176Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:a841ea91bef01fbf4ae64695a7f96cc0e0a04219e19f33db5587276251fe6536","observation_id":"00bbd447-a0d4-4a40-b5e4-ff48bcfa4d48","resolution":{"observed_at":"2026-08-03T15:17:02.918176Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:03.005362Z","title":"Lagemann, S.L Brunton, W","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:03.005362Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:b265d7b3a4741e75a5acfc357aae5f7cf256bbf55b2cce0e169130a5d5c742d1","observation_id":"dbde3e65-6c99-4051-9d59-4dbba2ba6d0f","resolution":{"observed_at":"2026-08-03T15:17:03.005362Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:03.093135Z","title":"Impact of reynolds number on the drag reduction mechanism of span- wise travelling surface waves.Flow, Turbulence and Combustion, 113(1):27–40, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:03.093135Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:d3d5fdad179dc3f50a118120358351492fd4cdedd73703b27c804e69d0ccd715","observation_id":"fe33e432-b3c4-4be2-b377-d16cd7698b4e","resolution":{"observed_at":"2026-08-03T15:17:03.093135Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:03.180523Z","title":"Rabault, M","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:03.180523Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:e87e2778e20acf66d8f7243676847e4492295ed930fb7b0d714e9ab6995db376","observation_id":"10e71d8b-b3e5-4a27-8562-3e1d99666485","resolution":{"observed_at":"2026-08-03T15:17:03.180523Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:03.258851Z","title":"Deep reinforcement learning con- trol of cylinder flow using rotary oscillations at low reynolds number.Energies, 13(22):5920, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:03.258851Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:fd6dbb07938b03c552ae15e4f64d502a7e16eb828f41b98a4837bb24da9d08c0","observation_id":"abc4ea37-da9e-4bf9-8795-441daceef1d3","resolution":{"observed_at":"2026-08-03T15:17:03.258851Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:03.339658Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:03.339658Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:c80d779480f5b59620a03eb384937201f203bf8962f1df3568d0230523963eff","observation_id":"3cdcbd0b-23ea-4be7-961a-2ed465b081ea","resolution":{"observed_at":"2026-08-03T15:17:03.339658Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:03.437005Z","title":"Rabault and Alexander Kuhnle","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:03.437005Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:3cff885525df7a2c5d01eaf7bd92822bafd492bb157a04433d348a508cf48ea7","observation_id":"df4f0494-e71b-4c06-b4e4-316181324dd7","resolution":{"observed_at":"2026-08-03T15:17:03.437005Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:03.560731Z","title":"Rabault, and B","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:03.560731Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:54ecb7c4849d6e3fdb172bce1a3a75d355d856589fce36b590c129fcea03ffa2","observation_id":"f55b4a84-5a54-4d1f-a951-eb31cb99eb9c","resolution":{"observed_at":"2026-08-03T15:17:03.560731Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:03.644895Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:03.644895Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:0d7492967bdea99c5f8ae3528dd88f760f00ba67986f6fce136c06dacbcdb9ef","observation_id":"3b575f5a-3d88-4a09-b9ee-714127595523","resolution":{"observed_at":"2026-08-03T15:17:03.644895Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:03.886801Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:03.886801Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:5eeb6c7cb67ad5968ee865975f5dc1175e94113d4be7e7254c1dd6bb7771d497","observation_id":"b897985a-2aeb-4478-a56e-fd8ea669d8cd","resolution":{"observed_at":"2026-08-03T15:17:03.886801Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:04.103847Z","title":"Rabault, R","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:04.103847Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:faa4cded1084d9eb514d69a62add5906533e220b9844bb8f08bae3ad568eac69","observation_id":"4f58e114-d5f7-48bc-9ac0-f9d32a8c5caa","resolution":{"observed_at":"2026-08-03T15:17:04.103847Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.12543","last_updated":"2024-01-23T08:02:11Z","snapshot_observed_at":"2026-08-13T04:37:11.741705Z","submitted_at":"2024-01-23T08:02:11Z","title":"Deep reinforcement transfer learning for active flow control of a 3D square cylinder under state dimension mismatch","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.12543","snapshot_observed_at":"2026-08-03T15:17:04.257427Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:04.257427Z"},"links":{"cited_paper":"/paper/2401.12543","citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:328ab8cf795ecba7c819fb9c00b83caf515a946a15844fe14e37e009030f5b37","observation_id":"2ae130d0-3ac3-4525-b571-da27bd48c0b6","resolution":{"observed_at":"2026-08-03T15:17:04.257427Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:04.423680Z","title":"Active flow control for bluff body drag reduction using reinforcement learning with partial measurements.Journal of Fluid Mechanics, 981:A17, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:04.423680Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:7e18847c0e0436a8435970508382330ea0896a136ac0e888d96196738eb1304e","observation_id":"095039e9-b37b-4d14-b90f-d9fcc3981ce0","resolution":{"observed_at":"2026-08-03T15:17:04.423680Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:04.596398Z","title":"Robust and adaptive deep reinforcement learning for enhancing flow control around a square cylinder with varying reynolds numbers.Physics of Fluids, 36(5), 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:04.596398Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:24ea5da918b14eaf74a95a91249d33f4364868457de1cdbd90d4dae705d97d2c","observation_id":"ab256cb8-21ab-4a6c-9f7b-8d2ceb0bf6e0","resolution":{"observed_at":"2026-08-03T15:17:04.596398Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:04.802149Z","title":"Rabault, Alexander Kuhnle, Hassan Ghraieb, Aurélien Larcher, and Elie Hachem","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:04.802149Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:a682ead50251734089eed5939f51806ef859cfece6be1f54181846187a2e57bf","observation_id":"8dc80cb5-519d-4eec-8419-0a6a681e0dae","resolution":{"observed_at":"2026-08-03T15:17:04.802149Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:04.978559Z","title":"Rüttgers, M","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:04.978559Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:bcc9cbcd1eb174c62aaf1359a3105040bea48eeb105a3ce848a5efc7575b1e04","observation_id":"44a39e10-544c-4102-9dc5-4ca826597205","resolution":{"observed_at":"2026-08-03T15:17:04.978559Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:05.106902Z","title":"Aerodynamic optimization of airfoil based on deep reinforcement learning.Physics of Fluids, 35(3), 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:05.106902Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:3e2935c4da34a8e2b1700daee3c1fd275c28cb658c38b64559445903cfd99698","observation_id":"b0aaf8a0-b006-455c-9363-4fcfd1c281b9","resolution":{"observed_at":"2026-08-03T15:17:05.106902Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:05.310003Z","title":"A reinforce- ment learning approach to airfoil shape optimiza- tion.Scientific Reports, 13(1):9753, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:05.310003Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:ba925be26148b50ba1a1cf6c0e366052868d5ce157f9f03bffdcd4abb3fcae9a","observation_id":"5eda7688-b3e7-4cc9-88af-f54458e75228","resolution":{"observed_at":"2026-08-03T15:17:05.310003Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:05.434300Z","title":"Enhancing vehicle aerodynamics with deep reinforcement learning in voxelised models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:05.434300Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:4905ee763e64b20c3d3599c97c3a52bb35d8f287cd1c29a14b7da6b758d9b5b7","observation_id":"d2fd3034-a7c4-4518-b5a9-eab8714d25a9","resolution":{"observed_at":"2026-08-03T15:17:05.434300Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:05.577324Z","title":"Aerodynamics-guided machine learning for design optimization of electric vehicles.Commu- nications Engineering, 3(1):174, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:05.577324Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:8c19a16cff5a4abc26b1288a5dbf6034aadab8462be8cc402a48a14670365ca2","observation_id":"c7a33fd0-fb9f-4d2b-b141-c9a67aa87c7c","resolution":{"observed_at":"2026-08-03T15:17:05.577324Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:05.682720Z","title":"Deep-reinforcement-learning-based hull form opti- mization method for stealth submarine design.In- ternational Journal of Naval Architecture and Ocean Engineering, 16:100595, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:05.682720Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:21d35ba776ac1dfd4ac5ff11592d9d07e84b459f7cfeb526445cce82591991f6","observation_id":"76c6142a-6d43-467f-a1f8-af80282a0952","resolution":{"observed_at":"2026-08-03T15:17:05.682720Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:05.787362Z","title":"Oh, M.-J","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:05.787362Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:3da4fd8917321a37db4fe0dfd24d4285f1237b0e8d473e8e6e87db3a7f99f7b2","observation_id":"fc819cae-cdc9-4ea1-a5d7-0f7858f151f2","resolution":{"observed_at":"2026-08-03T15:17:05.787362Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:05.887640Z","title":"Xiang, H","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:05.887640Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:d466b8f30fbb9978917c4735f39a772afe9389ba999eb1e9b1a4238fc4b70936","observation_id":"46332453-22e2-4e45-adea-3ec303369c09","resolution":{"observed_at":"2026-08-03T15:17:05.887640Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:05.989065Z","title":"Closed- loop flow separation control using the deep Q net- work over airfoil.AIAA Journal, 58(10):4260–4270, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:05.989065Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:158d659face93bcca8bc9fb688d69387b816f3653616d0f06dcda19643f46e72","observation_id":"26cf1153-7ef0-45aa-99ce-fb3d8bbeb4fd","resolution":{"observed_at":"2026-08-03T15:17:05.989065Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:06.061323Z","title":"Garcia, A","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:06.061323Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:4b188f7111a012ee23079d7a7b943e4f459b034f72fb7ac94ea64997372b4fc0","observation_id":"5d4fb302-bd3c-4fce-b373-3bdd2e20d119","resolution":{"observed_at":"2026-08-03T15:17:06.061323Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:06.126866Z","title":"Guastoni, J","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:06.126866Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:8a0a37ca7e11bed11625538fe79e2fc78761d10acff91259de7448e0c7746913","observation_id":"0e8eed68-6f66-4251-a197-c23ceaa8541f","resolution":{"observed_at":"2026-08-03T15:17:06.126866Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:06.232904Z","title":"Turbulence control for drag reduction through deep reinforcement learning.Phys","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:06.232904Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:c7e175fa4bc7b625c847a204a5744c0deba33d51fba90170609ab415ecd22c99","observation_id":"7f5442cc-5da2-424a-be96-490b03986202","resolution":{"observed_at":"2026-08-03T15:17:06.232904Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:06.306328Z","title":"Reinforcement-learning-based control of turbulent channel flows at high Reynolds numbers.J","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:06.306328Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:9745dea25f22871108a7d26a67cd1f3250d9806ae16104f2c51cc86fda6bfad5","observation_id":"9a17980e-aace-4623-8c91-404db7736ab3","resolution":{"observed_at":"2026-08-03T15:17:06.306328Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:06.402768Z","title":"Reinforcement learning of con- trol strategies for reducing skin friction drag in a 16 fully developed turbulent channel flow.J","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:06.402768Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:273fa55738305266db600f0132ae9bb26ac1ed74dba832c78eb8d04f62f83aa2","observation_id":"787f4998-4eea-47c0-9b99-4dbafa2d2930","resolution":{"observed_at":"2026-08-03T15:17:06.402768Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:06.509147Z","title":"Novati, S","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:06.509147Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:f149567bd5e6ba9802538aacfdaaf983693a2ec3991dbd9def650bb43841c75a","observation_id":"2464e12b-62be-41df-920d-e914bbcc315b","resolution":{"observed_at":"2026-08-03T15:17:06.509147Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:06.593098Z","title":"Verma, G","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:06.593098Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:e1c0fbb7b9def15cdb61057fd1580a0b5594771fc08d907c850ccceb70e414d5","observation_id":"a8e4ad50-f8a1-48d0-8a53-833d2046f596","resolution":{"observed_at":"2026-08-03T15:17:06.593098Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:06.681686Z","title":"Reinforcement learning of a multi-link swimmer at low Reynolds numbers.Physics of Fluids, 35(3), 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":99,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:06.681686Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:f0722feacb8f74f912f0d6079649968da94644fa446f7fcb8bc78bb731fce062","observation_id":"d67b1f7a-4ff7-4728-9f30-2b35f5530b4c","resolution":{"observed_at":"2026-08-03T15:17:06.681686Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:06.786465Z","title":"Chemotactic navi- gation in robotic swimmers via reset-free hierarchi- cal reinforcement learning.Nat","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":100,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:06.786465Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:fecf52498103ceb6e6de08d4f190fbf9811cbdc6b603a27a22bc40a3752ba960","observation_id":"7c4649a6-9edd-4508-88b5-bdf7a741a7ef","resolution":{"observed_at":"2026-08-03T15:17:06.786465Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T15:17:06.858095Z","title":"Learning to school in dense configurations with multi-agent deep reinforcement learning.Bioin- spiration & Biomimetics, 18(1):015003, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics","version":2},"reference_index":101,"source":"pdf_text","source_observed_at":"2026-08-03T15:17:06.858095Z"},"links":{"citing_paper":"/paper/2512.17534"},"observation_digest":"sha256:8048d530ded6680a02534477ca7e2662215a152a540a2696dbadcdbfb287b205","observation_id":"afe5d50d-8ffb-4b71-a241-c3039117d5a2","resolution":{"observed_at":"2026-08-03T15:17:06.858095Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2512.17534","last_updated":"2026-06-30T16:25:53Z","latest_version":2,"primary_category":"physics.flu-dyn","snapshot_observed_at":"2026-08-12T06:28:33.651933Z","submitted_at":"2025-12-19T12:58:06Z","title":"The HydroGym Reinforcement Learning Platform for Fluid Dynamics"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":99,"verified_exact":1,"verified_fuzzy":0},"total_outbound_references":250},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 100 of 250 outbound references and 4 inbound Pith citation observations for arXiv:2512.17534."}