{"as_of":"2026-08-08T04:41:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:58c2e35a332970570c40ef3bc38edd3ee9785f4026817b31ba41e17b10737b7d","coverage":[{"denominator":31,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":31,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T15:42:56.041531Z","state":"measured"},{"denominator":31,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":31,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2505.14039/citation-record","integrity":"/paper/2505.14039/integrity","json":"/paper/2505.14039/citation-record.json","paper":"/paper/2505.14039"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:43:01.766043Z","title":"Neural operators for accelerating scientific simulations and design, Nature Reviews Physics 6, no","venue":null,"work_id":"7355328a-be7e-409a-b4bb-2fd4008d0e26","year":2024},"citing_paper":{"arxiv_id":"2505.14039","last_updated":"2025-05-20T07:37:03Z","snapshot_observed_at":"2026-08-07T22:16:37.360251Z","submitted_at":"2025-05-20T07:37:03Z","title":"Learning High-dimensional Ionic Model Dynamics Using Fourier Neural Operators","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T15:42:53.040836Z"},"links":{"citing_paper":"/paper/2505.14039"},"observation_digest":"sha256:b9c8524cf3153ce3840af3c41b60df1a949bf296cc338e682f1edcd1acb16078","observation_id":"4ec3fff8-a30d-4003-a43a-3f7e994a8f73","resolution":{"observed_at":"2026-08-07T15:43:01.813631Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:43:01.504985Z","title":null,"venue":null,"work_id":"f9c6c81d-fa87-49e3-9ed4-886242de2998","year":2023},"citing_paper":{"arxiv_id":"2505.14039","last_updated":"2025-05-20T07:37:03Z","snapshot_observed_at":"2026-08-07T22:16:37.360251Z","submitted_at":"2025-05-20T07:37:03Z","title":"Learning High-dimensional Ionic Model Dynamics Using Fourier Neural Operators","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T15:42:53.138040Z"},"links":{"citing_paper":"/paper/2505.14039"},"observation_digest":"sha256:cf37c7f1302ec6f51b06a776dbbf8c7a47aba68002b7307b8e7bb701779d3a6b","observation_id":"aa6b59b4-90ab-410a-96ef-643c10fd853a","resolution":{"observed_at":"2026-08-07T15:43:01.607951Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:43:01.269844Z","title":null,"venue":null,"work_id":"b1f52058-1b6a-468c-a05d-cb2678a5fcaf","year":2011},"citing_paper":{"arxiv_id":"2505.14039","last_updated":"2025-05-20T07:37:03Z","snapshot_observed_at":"2026-08-07T22:16:37.360251Z","submitted_at":"2025-05-20T07:37:03Z","title":"Learning High-dimensional Ionic Model Dynamics Using Fourier Neural Operators","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T15:42:53.246904Z"},"links":{"citing_paper":"/paper/2505.14039"},"observation_digest":"sha256:d8939f7900b1998668cd4b1b53cec13b7f9025807257ded2d6e086fb0f0d798a","observation_id":"30108fcb-5b8c-4806-b90b-1ad94f83feae","resolution":{"observed_at":"2026-08-07T15:43:01.352501Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T15:42:53.336678Z","title":"Kovachki, Matthew E","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.14039","last_updated":"2025-05-20T07:37:03Z","snapshot_observed_at":"2026-08-07T22:16:37.360251Z","submitted_at":"2025-05-20T07:37:03Z","title":"Learning High-dimensional Ionic Model Dynamics Using Fourier Neural Operators","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T15:42:53.336678Z"},"links":{"citing_paper":"/paper/2505.14039"},"observation_digest":"sha256:42f39fb606fd2fc2960d8c931c4916a8e2b7642cd7f45cb7417ae7c148eb813c","observation_id":"aba8aca0-6891-4c10-89f8-9800b2f7df6c","resolution":{"observed_at":"2026-08-07T15:42:53.336678Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.11403","last_updated":"2025-01-06T19:25:03Z","snapshot_observed_at":"2026-08-04T07:29:03.587218Z","submitted_at":"2024-04-17T14:08:17Z","title":"Six decades of the FitzHugh-Nagumo model: A guide through its spatio-temporal dynamics and influence across disciplines","version":4},"cited_work":{"arxiv_id":"2404.11403","doi":null,"metadata_source":"pith","pith_arxiv_id":"2404.11403","snapshot_observed_at":"2026-08-07T15:42:56.475927Z","title":"Six decades of the FitzHugh-Nagumo model: A guide through its spatio-temporal dynamics and influence across disciplines","venue":"nlin.PS","work_id":"59a3f79d-8d2b-4352-be67-da50b2eb3f2e","year":2024},"citing_paper":{"arxiv_id":"2505.14039","last_updated":"2025-05-20T07:37:03Z","snapshot_observed_at":"2026-08-07T22:16:37.360251Z","submitted_at":"2025-05-20T07:37:03Z","title":"Learning High-dimensional Ionic Model Dynamics Using Fourier Neural Operators","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T15:42:53.464279Z"},"links":{"cited_paper":"/paper/2404.11403","citing_paper":"/paper/2505.14039"},"observation_digest":"sha256:9230df9c2d77692dc9aedbee20866a25ca2d216350e14e4d73ad7b802011caf8","observation_id":"6adcaa1f-691c-46e2-9e8b-73d23ee7f2ef","resolution":{"observed_at":"2026-08-07T15:42:56.539710Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:43:01.041127Z","title":"Pavarino,Learning the Hodgkin–Huxley model with operator learning techniques, Computer Methods in Applied Mechanics and Engineering, volume 432, page 117381, 2024","venue":null,"work_id":"b56d852b-a229-4c89-bcea-51ea180e20a9","year":2024},"citing_paper":{"arxiv_id":"2505.14039","last_updated":"2025-05-20T07:37:03Z","snapshot_observed_at":"2026-08-07T22:16:37.360251Z","submitted_at":"2025-05-20T07:37:03Z","title":"Learning High-dimensional Ionic Model Dynamics Using Fourier Neural Operators","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T15:42:53.547032Z"},"links":{"citing_paper":"/paper/2505.14039"},"observation_digest":"sha256:f77afe62a944e809f3a49eb94ab9645c1d141ff736545750bf8c38be05787e01","observation_id":"f4ec5225-dacf-4337-986a-a9e7a8329274","resolution":{"observed_at":"2026-08-07T15:43:01.132212Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:43:00.836128Z","title":null,"venue":null,"work_id":"7fa5926d-6335-4b66-af03-e94d5fec2226","year":2024},"citing_paper":{"arxiv_id":"2505.14039","last_updated":"2025-05-20T07:37:03Z","snapshot_observed_at":"2026-08-07T22:16:37.360251Z","submitted_at":"2025-05-20T07:37:03Z","title":"Learning High-dimensional Ionic Model Dynamics Using Fourier Neural Operators","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T15:42:53.640995Z"},"links":{"citing_paper":"/paper/2505.14039"},"observation_digest":"sha256:5661658f5e43d56872c58712998b19234f3dbd6936ec35122e27f737852cfa64","observation_id":"9d157a1e-a384-4b0f-85c4-91a29d9a424b","resolution":{"observed_at":"2026-08-07T15:43:00.914161Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:43:00.686855Z","title":null,"venue":null,"work_id":"5408cd8f-b5de-40f8-abd0-ed944cb88dca","year":2022},"citing_paper":{"arxiv_id":"2505.14039","last_updated":"2025-05-20T07:37:03Z","snapshot_observed_at":"2026-08-07T22:16:37.360251Z","submitted_at":"2025-05-20T07:37:03Z","title":"Learning High-dimensional Ionic Model Dynamics Using Fourier Neural Operators","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T15:42:53.724330Z"},"links":{"citing_paper":"/paper/2505.14039"},"observation_digest":"sha256:59fe23f51248a3bdeb7d58ae34d1438c1d3d2603b18070817896ec6345dc6e70","observation_id":"5a6c154c-a9c4-4ec7-9052-b6aa53cd9088","resolution":{"observed_at":"2026-08-07T15:43:00.756337Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:43:00.465480Z","title":null,"venue":null,"work_id":"8aedd057-2a5a-4e83-8ac2-3d09868dbf5d","year":1961},"citing_paper":{"arxiv_id":"2505.14039","last_updated":"2025-05-20T07:37:03Z","snapshot_observed_at":"2026-08-07T22:16:37.360251Z","submitted_at":"2025-05-20T07:37:03Z","title":"Learning High-dimensional Ionic Model Dynamics Using Fourier Neural Operators","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T15:42:53.908331Z"},"links":{"citing_paper":"/paper/2505.14039"},"observation_digest":"sha256:4d4be7f3ad8f500b204c2349970d5ae52656d9fd19b13a1d32c4d24adfcb1ff9","observation_id":"9ed71f07-d0eb-4e70-a392-b1b897da61c4","resolution":{"observed_at":"2026-08-07T15:43:00.537832Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:43:00.301092Z","title":"Franzone, Luca F","venue":null,"work_id":"5daec0c1-44da-43e6-80b6-9c49a518ae57","year":2014},"citing_paper":{"arxiv_id":"2505.14039","last_updated":"2025-05-20T07:37:03Z","snapshot_observed_at":"2026-08-07T22:16:37.360251Z","submitted_at":"2025-05-20T07:37:03Z","title":"Learning High-dimensional Ionic Model Dynamics Using Fourier Neural Operators","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T15:42:54.032135Z"},"links":{"citing_paper":"/paper/2505.14039"},"observation_digest":"sha256:cc19d4f00d385fbf38dde366a99eb942814d605a5b0cdd5834e0f6b24d9ed483","observation_id":"e7b57c36-6ce5-4a16-944f-866af7ed584c","resolution":{"observed_at":"2026-08-07T15:43:00.419124Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2503.18087","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:42:56.357277Z","title":null,"venue":null,"work_id":"132e816a-79a5-4d03-b1bc-73fcb19b86e7","year":2025},"citing_paper":{"arxiv_id":"2505.14039","last_updated":"2025-05-20T07:37:03Z","snapshot_observed_at":"2026-08-07T22:16:37.360251Z","submitted_at":"2025-05-20T07:37:03Z","title":"Learning High-dimensional Ionic Model Dynamics Using Fourier Neural Operators","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T15:42:54.101905Z"},"links":{"citing_paper":"/paper/2505.14039"},"observation_digest":"sha256:366184d2c88e9becc06bec8d368f7d0aeeb274f8c842d6810b757c3b864eacc6","observation_id":"2f230394-57b9-475a-853f-6efb6c65427b","resolution":{"observed_at":"2026-08-07T15:42:56.397765Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:42:59.995188Z","title":"Karniadakis,Physics-informed deep neural operator networks, Machine Learning in Modeling and Simulation: Methods and Applications, 219–254, 2023","venue":null,"work_id":"84d5b109-c89a-45e9-b0a1-d681f8683d33","year":2023},"citing_paper":{"arxiv_id":"2505.14039","last_updated":"2025-05-20T07:37:03Z","snapshot_observed_at":"2026-08-07T22:16:37.360251Z","submitted_at":"2025-05-20T07:37:03Z","title":"Learning High-dimensional Ionic Model Dynamics Using Fourier Neural Operators","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T15:42:54.218327Z"},"links":{"citing_paper":"/paper/2505.14039"},"observation_digest":"sha256:30737df2e06bcec28ebedee8a939f66f168e4760c8db2a392308cd1e8d4ff2af","observation_id":"595525d4-a880-43cb-991c-3dd4d221a0a8","resolution":{"observed_at":"2026-08-07T15:43:00.117837Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:42:59.757199Z","title":"Hodgkin, Andrew F","venue":null,"work_id":"168e3b97-be1e-4bcb-9099-239917d45c8b","year":1952},"citing_paper":{"arxiv_id":"2505.14039","last_updated":"2025-05-20T07:37:03Z","snapshot_observed_at":"2026-08-07T22:16:37.360251Z","submitted_at":"2025-05-20T07:37:03Z","title":"Learning High-dimensional Ionic Model Dynamics Using Fourier Neural Operators","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T15:42:54.325746Z"},"links":{"citing_paper":"/paper/2505.14039"},"observation_digest":"sha256:0a5ae972521136b42ce71cd2cff0699b08ea1599c1b587c8fbc1a7ea742fa6fc","observation_id":"6c735cdf-f4a2-4af4-a141-4fa8ba84c81d","resolution":{"observed_at":"2026-08-07T15:42:59.865644Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:42:59.565292Z","title":"Izhikevich,Dynamical Systems in Neuroscience, MIT press, 2007","venue":null,"work_id":"b9726807-77e3-41e3-845e-a7fada203bdf","year":2007},"citing_paper":{"arxiv_id":"2505.14039","last_updated":"2025-05-20T07:37:03Z","snapshot_observed_at":"2026-08-07T22:16:37.360251Z","submitted_at":"2025-05-20T07:37:03Z","title":"Learning High-dimensional Ionic Model Dynamics Using Fourier Neural Operators","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T15:42:54.408314Z"},"links":{"citing_paper":"/paper/2505.14039"},"observation_digest":"sha256:a2eb8b1c3e21fe9b0e82136c172a6d7bd92d5dd0ea3b6d70cbfb147f95319d51","observation_id":"45697fc5-06bc-4a16-989a-f8d93ee4f3ec","resolution":{"observed_at":"2026-08-07T15:42:59.680966Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:42:59.295740Z","title":null,"venue":null,"work_id":"5f3eb860-d30c-433b-8d3a-63224ee9cfc0","year":2009},"citing_paper":{"arxiv_id":"2505.14039","last_updated":"2025-05-20T07:37:03Z","snapshot_observed_at":"2026-08-07T22:16:37.360251Z","submitted_at":"2025-05-20T07:37:03Z","title":"Learning High-dimensional Ionic Model Dynamics Using Fourier Neural Operators","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T15:42:54.529613Z"},"links":{"citing_paper":"/paper/2505.14039"},"observation_digest":"sha256:c7819d6351b301043d2d62aae5f512a2b95da3f7c1ab620504511355dfd6de19","observation_id":"8e99c6de-15ef-49b6-aaed-78e2206f4a84","resolution":{"observed_at":"2026-08-07T15:42:59.426423Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:42:58.917804Z","title":null,"venue":null,"work_id":"d2169617-8aed-4015-adc1-626f9eabb6ac","year":2023},"citing_paper":{"arxiv_id":"2505.14039","last_updated":"2025-05-20T07:37:03Z","snapshot_observed_at":"2026-08-07T22:16:37.360251Z","submitted_at":"2025-05-20T07:37:03Z","title":"Learning High-dimensional Ionic Model Dynamics Using Fourier Neural Operators","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T15:42:54.668421Z"},"links":{"citing_paper":"/paper/2505.14039"},"observation_digest":"sha256:2ad6b959426f3fb6244512f342e107af16853a2d71ab85c82fc33ebc2cc046cc","observation_id":"6e5fc2b6-15f5-40ea-ba30-dfacb3c24dc0","resolution":{"observed_at":"2026-08-07T15:42:59.055973Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2304.13221","last_updated":"2024-06-15T00:00:32Z","snapshot_observed_at":"2026-07-06T15:20:05.729156Z","submitted_at":"2023-04-26T01:03:11Z","title":"Nonlocality and Nonlinearity Implies Universality in Operator Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.13221","snapshot_observed_at":"2026-08-07T15:42:54.740163Z","title":"Stuart,Nonlocality and nonlinearity implies universality in operator learning, arXiv preprint arXiv:2304.13221, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.14039","last_updated":"2025-05-20T07:37:03Z","snapshot_observed_at":"2026-08-07T22:16:37.360251Z","submitted_at":"2025-05-20T07:37:03Z","title":"Learning High-dimensional Ionic Model Dynamics Using Fourier Neural Operators","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T15:42:54.740163Z"},"links":{"cited_paper":"/paper/2304.13221","citing_paper":"/paper/2505.14039"},"observation_digest":"sha256:854bd1b7d6813b92af98d38094c2da59fd0fd00bbb265d9c7e4151d2b0f4c6d6","observation_id":"e418bf8a-9cc3-4136-8bbf-aea85f64fd0f","resolution":{"observed_at":"2026-08-07T15:42:54.740163Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:42:58.679127Z","title":null,"venue":null,"work_id":"70cfa841-4c5c-410a-b0ad-08fe8f7552c6","year":2020},"citing_paper":{"arxiv_id":"2505.14039","last_updated":"2025-05-20T07:37:03Z","snapshot_observed_at":"2026-08-07T22:16:37.360251Z","submitted_at":"2025-05-20T07:37:03Z","title":"Learning High-dimensional Ionic Model Dynamics Using Fourier Neural Operators","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T15:42:54.837483Z"},"links":{"citing_paper":"/paper/2505.14039"},"observation_digest":"sha256:4e0a6b710f982ee6f4f8041633f539848b08b846bae5d417b51cef3f667a152b","observation_id":"20263dd8-5446-4bcc-a0c8-fa516ea7c170","resolution":{"observed_at":"2026-08-07T15:42:58.783929Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2205.13671","last_updated":"2023-04-27T21:01:23Z","snapshot_observed_at":"2026-07-06T13:14:29.961613Z","submitted_at":"2022-05-26T23:17:53Z","title":"Transformer for Partial Differential Equations' Operator Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.13671","snapshot_observed_at":"2026-08-07T15:42:54.944738Z","title":"Farimani,Transformer for partial differential equations’ operator learning, arXiv preprint arXiv:2205.13671, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.14039","last_updated":"2025-05-20T07:37:03Z","snapshot_observed_at":"2026-08-07T22:16:37.360251Z","submitted_at":"2025-05-20T07:37:03Z","title":"Learning High-dimensional Ionic Model Dynamics Using Fourier Neural Operators","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T15:42:54.944738Z"},"links":{"cited_paper":"/paper/2205.13671","citing_paper":"/paper/2505.14039"},"observation_digest":"sha256:7b34920a67a934884543ab6da834bd8e4e530d18a8af52e5fb32ae942592c193","observation_id":"9a4d1f8e-115b-487a-a970-86c2ed023a40","resolution":{"observed_at":"2026-08-07T15:42:54.944738Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.08895","last_updated":"2021-05-17T03:12:33Z","snapshot_observed_at":"2026-07-06T10:05:26.653366Z","submitted_at":"2020-10-18T00:34:21Z","title":"Fourier Neural Operator for Parametric Partial Differential Equations","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.08895","snapshot_observed_at":"2026-08-07T15:42:55.049306Z","title":"Stuart, Anima Anandkumar,Fourier neural operator for parametric partial differential equations, arXiv preprint arXiv:2010.08895, 2020","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2505.14039","last_updated":"2025-05-20T07:37:03Z","snapshot_observed_at":"2026-08-07T22:16:37.360251Z","submitted_at":"2025-05-20T07:37:03Z","title":"Learning High-dimensional Ionic Model Dynamics Using Fourier Neural Operators","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T15:42:55.049306Z"},"links":{"cited_paper":"/paper/2010.08895","citing_paper":"/paper/2505.14039"},"observation_digest":"sha256:21cd6d2f96fabb9f4915ca2a84e42b8fbe2240c82f79b3139289a8b7c8a55751","observation_id":"912d0b65-ba24-40ab-8c42-a6072e729448","resolution":{"observed_at":"2026-08-07T15:42:55.049306Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1807.05118","last_updated":"2018-07-13T15:00:17Z","snapshot_observed_at":"2026-07-06T06:50:03.471843Z","submitted_at":"2018-07-13T15:00:17Z","title":"Tune: A Research Platform for Distributed Model Selection and Training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.05118","snapshot_observed_at":"2026-08-07T15:42:55.118216Z","title":"Gonzalez, Ion Stoica,Tune: A Research Platform for Distributed Model Selection and Training, arXiv preprint arXiv:1807.05118, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.14039","last_updated":"2025-05-20T07:37:03Z","snapshot_observed_at":"2026-08-07T22:16:37.360251Z","submitted_at":"2025-05-20T07:37:03Z","title":"Learning High-dimensional Ionic Model Dynamics Using Fourier Neural Operators","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T15:42:55.118216Z"},"links":{"cited_paper":"/paper/1807.05118","citing_paper":"/paper/2505.14039"},"observation_digest":"sha256:17376d047dbf1be77ac94ee3233b2b669acfc327f05ebe0e45dd3ad6a1fa5333","observation_id":"3954ef6c-e13e-4ac1-8020-1ce40874a2ec","resolution":{"observed_at":"2026-08-07T15:42:55.118216Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.05101","last_updated":"2019-01-04T21:01:49Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-11-14T14:24:06Z","title":"Decoupled Weight Decay Regularization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.05101","snapshot_observed_at":"2026-08-07T15:42:55.253277Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.14039","last_updated":"2025-05-20T07:37:03Z","snapshot_observed_at":"2026-08-07T22:16:37.360251Z","submitted_at":"2025-05-20T07:37:03Z","title":"Learning High-dimensional Ionic Model Dynamics Using Fourier Neural Operators","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T15:42:55.253277Z"},"links":{"cited_paper":"/paper/1711.05101","citing_paper":"/paper/2505.14039"},"observation_digest":"sha256:86da934395dd334c3e885fc1cb6f7dea91d177fccadb8e57c8570a04c37fbc01","observation_id":"0855d862-40d3-4fba-9026-05a2ba01520a","resolution":{"observed_at":"2026-08-07T15:42:55.253277Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:42:58.440030Z","title":"Karniadakis,Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators, Nature Machine Intelligence, 3, 3, 218–229, 2021","venue":null,"work_id":"1d2b070b-1fc2-484b-86fe-5e5c3ca8edff","year":2021},"citing_paper":{"arxiv_id":"2505.14039","last_updated":"2025-05-20T07:37:03Z","snapshot_observed_at":"2026-08-07T22:16:37.360251Z","submitted_at":"2025-05-20T07:37:03Z","title":"Learning High-dimensional Ionic Model Dynamics Using Fourier Neural Operators","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T15:42:55.424151Z"},"links":{"citing_paper":"/paper/2505.14039"},"observation_digest":"sha256:ed249eb7db963fd20fba636bf5b1d5b139b6c2712c4c77835ba769f17cfde4d4","observation_id":"4ae14fe2-3fb5-456d-91cc-e7c7d3899dc6","resolution":{"observed_at":"2026-08-07T15:42:58.567597Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:42:58.062658Z","title":null,"venue":null,"work_id":"cbf795b4-5ae7-45e6-974e-a095e33091a4","year":2011},"citing_paper":{"arxiv_id":"2505.14039","last_updated":"2025-05-20T07:37:03Z","snapshot_observed_at":"2026-08-07T22:16:37.360251Z","submitted_at":"2025-05-20T07:37:03Z","title":"Learning High-dimensional Ionic Model Dynamics Using Fourier Neural Operators","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T15:42:55.543186Z"},"links":{"citing_paper":"/paper/2505.14039"},"observation_digest":"sha256:98fc80c3e70ac9a3f7f92e3c65efa00f2185e21cd2848224fc00b3932e71973c","observation_id":"0c483c20-6b9c-4a12-9057-858bc4d2056d","resolution":{"observed_at":"2026-08-07T15:42:58.189257Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:42:57.756485Z","title":null,"venue":null,"work_id":"ce219f0a-741c-40a5-92ed-d4f7ee376e89","year":2019},"citing_paper":{"arxiv_id":"2505.14039","last_updated":"2025-05-20T07:37:03Z","snapshot_observed_at":"2026-08-07T22:16:37.360251Z","submitted_at":"2025-05-20T07:37:03Z","title":"Learning High-dimensional Ionic Model Dynamics Using Fourier Neural Operators","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T15:42:55.641193Z"},"links":{"citing_paper":"/paper/2505.14039"},"observation_digest":"sha256:ce737c41745ecd622f761a7bc60345dee3a31412d32cd1cfc3f2ce7024f1dc3c","observation_id":"52bf5165-66e9-4bfe-a869-b02d66554e07","resolution":{"observed_at":"2026-08-07T15:42:57.935428Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:42:57.579903Z","title":null,"venue":null,"work_id":"1fa40dc1-56e7-4ccc-9db0-ca1ed3ab322c","year":2023},"citing_paper":{"arxiv_id":"2505.14039","last_updated":"2025-05-20T07:37:03Z","snapshot_observed_at":"2026-08-07T22:16:37.360251Z","submitted_at":"2025-05-20T07:37:03Z","title":"Learning High-dimensional Ionic Model Dynamics Using Fourier Neural Operators","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T15:42:55.696080Z"},"links":{"citing_paper":"/paper/2505.14039"},"observation_digest":"sha256:ffbb79700c3a25d2307b4722946d70025c6c06181805a9254da01c18ea458233","observation_id":"f46912d2-cce2-4bef-bf36-bab17b5b5baf","resolution":{"observed_at":"2026-08-07T15:42:57.677151Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:42:57.299862Z","title":null,"venue":null,"work_id":"832a2fd4-d08c-4c73-8ec3-0b0d815acf7d","year":2024},"citing_paper":{"arxiv_id":"2505.14039","last_updated":"2025-05-20T07:37:03Z","snapshot_observed_at":"2026-08-07T22:16:37.360251Z","submitted_at":"2025-05-20T07:37:03Z","title":"Learning High-dimensional Ionic Model Dynamics Using Fourier Neural Operators","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T15:42:55.770847Z"},"links":{"citing_paper":"/paper/2505.14039"},"observation_digest":"sha256:016a96846312b7d01119bedb1d9727aaf67a43314177eab1f023dfc6f4ed0b24","observation_id":"362946b0-5ff8-47f7-8995-6bef5ec8aba1","resolution":{"observed_at":"2026-08-07T15:42:57.396871Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:42:56.991791Z","title":"Shampine, Mark W","venue":null,"work_id":"801d7836-41fe-42fd-aa31-6378d93a68a8","year":1999},"citing_paper":{"arxiv_id":"2505.14039","last_updated":"2025-05-20T07:37:03Z","snapshot_observed_at":"2026-08-07T22:16:37.360251Z","submitted_at":"2025-05-20T07:37:03Z","title":"Learning High-dimensional Ionic Model Dynamics Using Fourier Neural Operators","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T15:42:55.833183Z"},"links":{"citing_paper":"/paper/2505.14039"},"observation_digest":"sha256:b20f50c9da01e77fee02516218659ea1182423124c309ed0bbf978b37528c71e","observation_id":"c026c34c-47de-4546-b203-0d10f535d592","resolution":{"observed_at":"2026-08-07T15:42:57.136699Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:42:56.807819Z","title":null,"venue":null,"work_id":"10d4ff81-2290-49ad-9981-94af44382a0d","year":2024},"citing_paper":{"arxiv_id":"2505.14039","last_updated":"2025-05-20T07:37:03Z","snapshot_observed_at":"2026-08-07T22:16:37.360251Z","submitted_at":"2025-05-20T07:37:03Z","title":"Learning High-dimensional Ionic Model Dynamics Using Fourier Neural Operators","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T15:42:55.914200Z"},"links":{"citing_paper":"/paper/2505.14039"},"observation_digest":"sha256:a07044c696ecfc6f8ab9d46f6f3cbe42e38e17f7c834282fe86c21b0f472e327","observation_id":"3e64a4e8-f5c6-4cae-a926-ed0a1ad4cc31","resolution":{"observed_at":"2026-08-07T15:42:56.891795Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:42:56.695352Z","title":null,"venue":null,"work_id":"b1861c59-4aab-4c2f-8117-3927c20272cc","year":2023},"citing_paper":{"arxiv_id":"2505.14039","last_updated":"2025-05-20T07:37:03Z","snapshot_observed_at":"2026-08-07T22:16:37.360251Z","submitted_at":"2025-05-20T07:37:03Z","title":"Learning High-dimensional Ionic Model Dynamics Using Fourier Neural Operators","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T15:42:55.976468Z"},"links":{"citing_paper":"/paper/2505.14039"},"observation_digest":"sha256:773bda4d7c43ea418dfb836362f55f2f83ee8bc6ccec993163a669144b5b5262","observation_id":"f1d8656c-60e7-4635-9a7a-70c87ac1f877","resolution":{"observed_at":"2026-08-07T15:42:56.732742Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.02366","last_updated":"2024-06-01T15:33:37Z","snapshot_observed_at":"2026-08-02T06:48:35.071514Z","submitted_at":"2024-02-04T06:37:38Z","title":"Transolver: A Fast Transformer Solver for PDEs on General Geometries","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.02366","snapshot_observed_at":"2026-08-07T15:42:56.041531Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.14039","last_updated":"2025-05-20T07:37:03Z","snapshot_observed_at":"2026-08-07T22:16:37.360251Z","submitted_at":"2025-05-20T07:37:03Z","title":"Learning High-dimensional Ionic Model Dynamics Using Fourier Neural Operators","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T15:42:56.041531Z"},"links":{"cited_paper":"/paper/2402.02366","citing_paper":"/paper/2505.14039"},"observation_digest":"sha256:c9547f093378da95f69eaa994b124584ed57a781787a11f281108cbccc826caf","observation_id":"726dd1d4-68ea-42a4-b611-c2a09d412014","resolution":{"observed_at":"2026-08-07T15:42:56.041531Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.14039","last_updated":"2025-05-20T07:37:03Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T22:16:37.360251Z","submitted_at":"2025-05-20T07:37:03Z","title":"Learning High-dimensional Ionic Model Dynamics Using Fourier Neural Operators"},"reference_resolution":{"displayed":31,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":21,"verified_exact":2,"verified_fuzzy":8},"total_outbound_references":31},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2505.14039."}