{"as_of":"2026-08-19T05:32:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:03a6f61ac9555c80f85291ca8e88f2c6958d8861f0b5b951ff332943aaf55244","coverage":[{"denominator":32,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":32,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T00:07:11.571870Z","state":"measured"},{"denominator":32,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":32,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+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/2505.03021/citation-record","integrity":"/paper/2505.03021/integrity","json":"/paper/2505.03021/citation-record.json","paper":"/paper/2505.03021"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T00:07:11.939716Z","title":"Training material models using gradient descent algorithms","venue":null,"work_id":"b06b56c3-4251-4045-a728-19e62bb06a43","year":2023},"citing_paper":{"arxiv_id":"2505.03021","last_updated":"2025-05-05T20:44:42Z","snapshot_observed_at":"2026-08-15T23:59:32.131198Z","submitted_at":"2025-05-05T20:44:42Z","title":"A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-16T00:07:11.452019Z"},"links":{"citing_paper":"/paper/2505.03021"},"observation_digest":"sha256:a3e9804edfdf1ab141113e1ed7c44143d4a54f30fafe18bb5021616a875818d4","observation_id":"62387d8e-33f2-4706-95ba-adf481c532e8","resolution":{"observed_at":"2026-08-16T00:07:11.942872Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T00:07:11.928709Z","title":"An advancement in cyclic plasticity modeling for multiaxial ratcheting simulation,","venue":null,"work_id":"239fbcc8-7052-4e3c-bc9a-3042bd8e6a70","year":2002},"citing_paper":{"arxiv_id":"2505.03021","last_updated":"2025-05-05T20:44:42Z","snapshot_observed_at":"2026-08-15T23:59:32.131198Z","submitted_at":"2025-05-05T20:44:42Z","title":"A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-16T00:07:11.456317Z"},"links":{"citing_paper":"/paper/2505.03021"},"observation_digest":"sha256:0583fecd048b60452d67b4acd6b58ef82700943161f632bab6a98c04f38f0172","observation_id":"7f61bc4d-cc93-4b23-98d1-52102ede61b6","resolution":{"observed_at":"2026-08-16T00:07:11.932265Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T00:07:11.918024Z","title":"Anatomy of coupled constitutive models for ratcheting simulation,","venue":null,"work_id":"9dfc706a-aa95-4a3c-9f6e-e09c945f97c2","year":2000},"citing_paper":{"arxiv_id":"2505.03021","last_updated":"2025-05-05T20:44:42Z","snapshot_observed_at":"2026-08-15T23:59:32.131198Z","submitted_at":"2025-05-05T20:44:42Z","title":"A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-16T00:07:11.461023Z"},"links":{"citing_paper":"/paper/2505.03021"},"observation_digest":"sha256:3f0bfd2a1e788d7892bb87f766366ea14353016520addd6f13ad9af9bbbdfbb2","observation_id":"88687193-8c03-4c59-aaee-4c2eb02af7c6","resolution":{"observed_at":"2026-08-16T00:07:11.921807Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T00:07:11.908098Z","title":"Implicit constitutive modelling for viscoplasticity using neural networks,","venue":null,"work_id":"b621c784-5094-4232-b91f-b605852f2edd","year":1998},"citing_paper":{"arxiv_id":"2505.03021","last_updated":"2025-05-05T20:44:42Z","snapshot_observed_at":"2026-08-15T23:59:32.131198Z","submitted_at":"2025-05-05T20:44:42Z","title":"A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-16T00:07:11.465508Z"},"links":{"citing_paper":"/paper/2505.03021"},"observation_digest":"sha256:b5b72aecc00da28a06867c15231e6f68054a1ab446f592ca73dccefac97e26f9","observation_id":"d0d68a77-bb41-4559-b4e2-1a3d2aac2dfa","resolution":{"observed_at":"2026-08-16T00:07:11.911854Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T00:07:11.896944Z","title":"Development of LSTM networks for predicting viscoplasticity with effects of deformation, strain rate, and temperature history,","venue":null,"work_id":"31010ec8-1f6d-42f3-8343-7ff79e68526a","year":2021},"citing_paper":{"arxiv_id":"2505.03021","last_updated":"2025-05-05T20:44:42Z","snapshot_observed_at":"2026-08-15T23:59:32.131198Z","submitted_at":"2025-05-05T20:44:42Z","title":"A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-16T00:07:11.469729Z"},"links":{"citing_paper":"/paper/2505.03021"},"observation_digest":"sha256:0f3424eedef5deb96502908570df5e4f4b702f55343b9f3e50fb8ee98e447d51","observation_id":"4124d5cc-622c-4981-b9b8-6416adc4ec33","resolution":{"observed_at":"2026-08-16T00:07:11.900845Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T00:07:11.886137Z","title":"Numerical characterisation of uncured elastomers by a neural network based approach,","venue":null,"work_id":"15ad9d93-30ab-490c-8dcb-3c4bbe6030a7","year":2017},"citing_paper":{"arxiv_id":"2505.03021","last_updated":"2025-05-05T20:44:42Z","snapshot_observed_at":"2026-08-15T23:59:32.131198Z","submitted_at":"2025-05-05T20:44:42Z","title":"A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-16T00:07:11.474233Z"},"links":{"citing_paper":"/paper/2505.03021"},"observation_digest":"sha256:98206f333fb3e7f78e5e9d0e60ee3712da19f9ee6d3b222e6380692f12018275","observation_id":"7cc94e3a-176d-4da8-956f-8615570408e1","resolution":{"observed_at":"2026-08-16T00:07:11.890223Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T00:07:11.872834Z","title":"Deep learning predicts path-dependent plasticity,","venue":null,"work_id":"1bec95b8-d3d9-42c9-a4dd-f4606c66b4c6","year":2019},"citing_paper":{"arxiv_id":"2505.03021","last_updated":"2025-05-05T20:44:42Z","snapshot_observed_at":"2026-08-15T23:59:32.131198Z","submitted_at":"2025-05-05T20:44:42Z","title":"A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-16T00:07:11.479395Z"},"links":{"citing_paper":"/paper/2505.03021"},"observation_digest":"sha256:d581a1c6fcc5b2279fc1d01ef659357c8ec2fb73bd3f876d1f09c69932f5b2df","observation_id":"f5a07256-7051-4950-9b24-801aa09432aa","resolution":{"observed_at":"2026-08-16T00:07:11.878563Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T00:07:11.862080Z","title":null,"venue":null,"work_id":"1aa7c3c5-ea9a-4e62-a380-f12f43738604","year":2013},"citing_paper":{"arxiv_id":"2505.03021","last_updated":"2025-05-05T20:44:42Z","snapshot_observed_at":"2026-08-15T23:59:32.131198Z","submitted_at":"2025-05-05T20:44:42Z","title":"A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-16T00:07:11.482889Z"},"links":{"citing_paper":"/paper/2505.03021"},"observation_digest":"sha256:e1cc9ad59f44508530fc320430b9d53ca5d72872eb4f17ad8e039b3817d6a76d","observation_id":"06925a43-9d95-46c3-a746-99d03115df7c","resolution":{"observed_at":"2026-08-16T00:07:11.865725Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T00:07:11.850800Z","title":"Elastoplastic constitutive modeling under the complex loading driven by GRU and small-amount data,","venue":null,"work_id":"e51a2913-74d2-43bf-83ed-e5f54df27852","year":2022},"citing_paper":{"arxiv_id":"2505.03021","last_updated":"2025-05-05T20:44:42Z","snapshot_observed_at":"2026-08-15T23:59:32.131198Z","submitted_at":"2025-05-05T20:44:42Z","title":"A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-16T00:07:11.485949Z"},"links":{"citing_paper":"/paper/2505.03021"},"observation_digest":"sha256:8597face83d55d7cd3e7a8a98860b40ff658001bd96b3d1526cd0fe00a9e8769","observation_id":"99169dfa-723c-4151-91db-4bdb35b36154","resolution":{"observed_at":"2026-08-16T00:07:11.855032Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T00:07:11.836949Z","title":"Deep residual learning for image recognition,","venue":null,"work_id":"13fa133a-8f35-41ef-ba2e-ade9c521131d","year":2016},"citing_paper":{"arxiv_id":"2505.03021","last_updated":"2025-05-05T20:44:42Z","snapshot_observed_at":"2026-08-15T23:59:32.131198Z","submitted_at":"2025-05-05T20:44:42Z","title":"A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-16T00:07:11.489621Z"},"links":{"citing_paper":"/paper/2505.03021"},"observation_digest":"sha256:2ca3638faac8a3d290e108554db21e24378db39fdbfda33cba94c1b4d43d5fe3","observation_id":"f69c74e4-b39e-4e02-8e9e-ace032ea804f","resolution":{"observed_at":"2026-08-16T00:07:11.843585Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T00:07:11.824861Z","title":"Neural ordinary differential equations,","venue":null,"work_id":"221746bc-65b8-4d93-9922-991de66b80e6","year":2018},"citing_paper":{"arxiv_id":"2505.03021","last_updated":"2025-05-05T20:44:42Z","snapshot_observed_at":"2026-08-15T23:59:32.131198Z","submitted_at":"2025-05-05T20:44:42Z","title":"A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-16T00:07:11.493596Z"},"links":{"citing_paper":"/paper/2505.03021"},"observation_digest":"sha256:77b672c513dcf7db4a9f680e6c19b3623581b8cfdb0217632b9b8a1468bb947c","observation_id":"249dec90-707a-484d-bb3f-ef73e681ee89","resolution":{"observed_at":"2026-08-16T00:07:11.828155Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1910.00753","last_updated":"2019-10-02T02:42:34Z","snapshot_observed_at":"2026-08-15T17:16:47.950921Z","submitted_at":"2019-10-02T02:42:34Z","title":"Equivariant Flows: sampling configurations for multi-body systems with symmetric energies","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.00753","snapshot_observed_at":"2026-08-16T00:07:11.498008Z","title":"Equivariant flows: sampling configurations for multi -body systems with symmetric energies,","venue":null,"work_id":null,"year":1910},"citing_paper":{"arxiv_id":"2505.03021","last_updated":"2025-05-05T20:44:42Z","snapshot_observed_at":"2026-08-15T23:59:32.131198Z","submitted_at":"2025-05-05T20:44:42Z","title":"A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-16T00:07:11.498008Z"},"links":{"cited_paper":"/paper/1910.00753","citing_paper":"/paper/2505.03021"},"observation_digest":"sha256:1cc20a75bfb8d6fae829a438aaa518762c2691202799ca24439f3752704e58bd","observation_id":"ebfa18da-d3a4-4200-af3b-a1ef03792056","resolution":{"observed_at":"2026-08-16T00:07:11.498008Z","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":"2026-08-16T00:07:11.812879Z","title":"Latent ordinary differential equations for irregularly-sampled time series,","venue":null,"work_id":"84b7e7cc-7c9c-46f4-b81b-4b34e48d4d61","year":2019},"citing_paper":{"arxiv_id":"2505.03021","last_updated":"2025-05-05T20:44:42Z","snapshot_observed_at":"2026-08-15T23:59:32.131198Z","submitted_at":"2025-05-05T20:44:42Z","title":"A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-16T00:07:11.502346Z"},"links":{"citing_paper":"/paper/2505.03021"},"observation_digest":"sha256:be0afb894ad6ce31ed9796aaa8a4afada5ff748ef66eab053dcc9b7de8ac4852","observation_id":"abb59d1e-acad-4bdb-9157-d31650a415f3","resolution":{"observed_at":"2026-08-16T00:07:11.817053Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T00:07:11.802483Z","title":"Scalable gradients and variational inference for stochastic differential equations,","venue":null,"work_id":"11d61f02-b014-4a5c-97a6-e54bad5d9b10","year":2020},"citing_paper":{"arxiv_id":"2505.03021","last_updated":"2025-05-05T20:44:42Z","snapshot_observed_at":"2026-08-15T23:59:32.131198Z","submitted_at":"2025-05-05T20:44:42Z","title":"A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-16T00:07:11.505305Z"},"links":{"citing_paper":"/paper/2505.03021"},"observation_digest":"sha256:916276bec12d08869c05f0a202a45e3c5dcc58f1fd36e96add941f77c3e174ab","observation_id":"3c52f8ff-de6c-481f-84b5-f08a17a0c987","resolution":{"observed_at":"2026-08-16T00:07:11.805782Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T00:07:11.791855Z","title":"Training material models using gradient descent algorithms,","venue":null,"work_id":"edb1f36c-b5b2-4200-a9da-9e3a087fae5c","year":2023},"citing_paper":{"arxiv_id":"2505.03021","last_updated":"2025-05-05T20:44:42Z","snapshot_observed_at":"2026-08-15T23:59:32.131198Z","submitted_at":"2025-05-05T20:44:42Z","title":"A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-16T00:07:11.508947Z"},"links":{"citing_paper":"/paper/2505.03021"},"observation_digest":"sha256:4d11f13b7ee6e380ab4ce558ef81f10ab352e56c0bd90602e6933b95026cc513","observation_id":"155881f4-936c-4f4a-9dc7-ccefcd461567","resolution":{"observed_at":"2026-08-16T00:07:11.795669Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T00:07:11.781304Z","title":"Automatic differentiation in pytorch,","venue":null,"work_id":"62daf238-5d6d-4a57-a6ce-37a63ff6709e","year":2017},"citing_paper":{"arxiv_id":"2505.03021","last_updated":"2025-05-05T20:44:42Z","snapshot_observed_at":"2026-08-15T23:59:32.131198Z","submitted_at":"2025-05-05T20:44:42Z","title":"A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-16T00:07:11.512524Z"},"links":{"citing_paper":"/paper/2505.03021"},"observation_digest":"sha256:9450196e04dfe3f6339e6896489dc5c3c5f957912993d790079a13bb7bdd2d07","observation_id":"a20a1364-6677-4618-adad-50c120f3e8c6","resolution":{"observed_at":"2026-08-16T00:07:11.784482Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T00:07:11.770220Z","title":"Hierarchical deep -learning neural networks: finite elements and beyond,","venue":null,"work_id":"4f142f1a-81e8-4648-8125-9a86abb17c2d","year":2021},"citing_paper":{"arxiv_id":"2505.03021","last_updated":"2025-05-05T20:44:42Z","snapshot_observed_at":"2026-08-15T23:59:32.131198Z","submitted_at":"2025-05-05T20:44:42Z","title":"A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-16T00:07:11.515900Z"},"links":{"citing_paper":"/paper/2505.03021"},"observation_digest":"sha256:5bd9608307edc470e1da34c2f941aa6887365d8e2dc1b701abc9d43e3a907c40","observation_id":"53851318-071c-4ecf-87ba-0fe993b7e025","resolution":{"observed_at":"2026-08-16T00:07:11.773791Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T00:07:11.758448Z","title":"HiDeNN-FEM: a seamless machine learning approach to nonlinear finite element analysis,","venue":null,"work_id":"37e07103-4091-46bb-af05-ec4903c8d14a","year":2023},"citing_paper":{"arxiv_id":"2505.03021","last_updated":"2025-05-05T20:44:42Z","snapshot_observed_at":"2026-08-15T23:59:32.131198Z","submitted_at":"2025-05-05T20:44:42Z","title":"A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-16T00:07:11.519790Z"},"links":{"citing_paper":"/paper/2505.03021"},"observation_digest":"sha256:8327a2474a6c27f6469e261c1506e12d7036070123dfab59e0b289e327621992","observation_id":"ba1e814a-e59b-46b0-8c46-755d1f5a1387","resolution":{"observed_at":"2026-08-16T00:07:11.762575Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T00:07:11.747983Z","title":"Convolution Hierarchical Deep-Learning Neural Network Tensor Decomposition (C- HiDeNN-TD) for high -resolution topology optimization,","venue":null,"work_id":"0c7fc5b1-7a00-445e-bfec-02e2c28c09c0","year":2023},"citing_paper":{"arxiv_id":"2505.03021","last_updated":"2025-05-05T20:44:42Z","snapshot_observed_at":"2026-08-15T23:59:32.131198Z","submitted_at":"2025-05-05T20:44:42Z","title":"A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-16T00:07:11.523722Z"},"links":{"citing_paper":"/paper/2505.03021"},"observation_digest":"sha256:ddef106cda7f5f7a1f9e149e3446307a47586f255ad99aa5da09c2333c8e60d9","observation_id":"8632bff0-7d99-4a7b-8d18-a9fc8a2173b1","resolution":{"observed_at":"2026-08-16T00:07:11.752223Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T00:07:11.737395Z","title":"Semi -supervised invertible neural operators for Bayesian inverse problems,","venue":null,"work_id":"51338284-5845-4b21-b33c-fed2ad0e79fa","year":2023},"citing_paper":{"arxiv_id":"2505.03021","last_updated":"2025-05-05T20:44:42Z","snapshot_observed_at":"2026-08-15T23:59:32.131198Z","submitted_at":"2025-05-05T20:44:42Z","title":"A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-16T00:07:11.527595Z"},"links":{"citing_paper":"/paper/2505.03021"},"observation_digest":"sha256:bd5b5de69be162c0305db9008401d56614d2a77132bcf9e8f438a31b351cdbdb","observation_id":"3d8bb3ee-e3e0-4181-9549-9173522b28b1","resolution":{"observed_at":"2026-08-16T00:07:11.741445Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T00:07:11.726707Z","title":"Information processing, data inferences, and scientific generalization,","venue":null,"work_id":"e3852c13-bd8b-4106-8bb4-7a4363775cb8","year":1974},"citing_paper":{"arxiv_id":"2505.03021","last_updated":"2025-05-05T20:44:42Z","snapshot_observed_at":"2026-08-15T23:59:32.131198Z","submitted_at":"2025-05-05T20:44:42Z","title":"A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-16T00:07:11.530998Z"},"links":{"citing_paper":"/paper/2505.03021"},"observation_digest":"sha256:42eced85272ef111b4e361ac9ad28d8f393eabb059b7b917538aada31b032f6d","observation_id":"07846e73-c43a-4799-b0a8-85eba1c143ce","resolution":{"observed_at":"2026-08-16T00:07:11.730626Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T00:07:11.715566Z","title":"BACON. 5: The discovery of conservation laws,","venue":null,"work_id":"f8c2f3d1-50bd-428d-808e-4d2c47dfc8a7","year":1981},"citing_paper":{"arxiv_id":"2505.03021","last_updated":"2025-05-05T20:44:42Z","snapshot_observed_at":"2026-08-15T23:59:32.131198Z","submitted_at":"2025-05-05T20:44:42Z","title":"A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-16T00:07:11.534441Z"},"links":{"citing_paper":"/paper/2505.03021"},"observation_digest":"sha256:910bdc15444fbbaec3702275f0811c31607591d8c0f6e6e46f97508079db6474","observation_id":"560b82a8-a556-4b9f-8558-62024f602fca","resolution":{"observed_at":"2026-08-16T00:07:11.719455Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T00:07:11.704573Z","title":"Genetic programming as a means for programming computers by natural selection,","venue":null,"work_id":"2afdd818-6c95-4f25-95bf-744d93b9909e","year":1994},"citing_paper":{"arxiv_id":"2505.03021","last_updated":"2025-05-05T20:44:42Z","snapshot_observed_at":"2026-08-15T23:59:32.131198Z","submitted_at":"2025-05-05T20:44:42Z","title":"A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-16T00:07:11.538280Z"},"links":{"citing_paper":"/paper/2505.03021"},"observation_digest":"sha256:58fa36ebc8a453e25e78773b6b664dcfe8cc77733abded1e00ec9b67f150a237","observation_id":"12d90c32-c19d-423d-a6b9-30d50a435c19","resolution":{"observed_at":"2026-08-16T00:07:11.707891Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T00:07:11.692373Z","title":"On the limited memory BFGS method for large scale optimization,","venue":null,"work_id":"4351869c-122e-4dc0-a5bc-f037787ce02c","year":1989},"citing_paper":{"arxiv_id":"2505.03021","last_updated":"2025-05-05T20:44:42Z","snapshot_observed_at":"2026-08-15T23:59:32.131198Z","submitted_at":"2025-05-05T20:44:42Z","title":"A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-16T00:07:11.542307Z"},"links":{"citing_paper":"/paper/2505.03021"},"observation_digest":"sha256:8fff3a9d33a54743f1b2b9606355c726dcb65a543dd4d59ef98d458bbf93e880","observation_id":"e66cf6ba-fa97-4204-a4d1-3e01432bd611","resolution":{"observed_at":"2026-08-16T00:07:11.696071Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T00:07:11.680934Z","title":"Training material models using gradient descent algorithms,","venue":null,"work_id":"d7eb79b6-60c3-4453-a4c1-c5314327fad2","year":2023},"citing_paper":{"arxiv_id":"2505.03021","last_updated":"2025-05-05T20:44:42Z","snapshot_observed_at":"2026-08-15T23:59:32.131198Z","submitted_at":"2025-05-05T20:44:42Z","title":"A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-16T00:07:11.546231Z"},"links":{"citing_paper":"/paper/2505.03021"},"observation_digest":"sha256:f86e9977468a160605aff51ee848810d0a31f0639298819997d1296643840965","observation_id":"26d9fff4-8329-4ae2-8ab0-bbb6eac0090d","resolution":{"observed_at":"2026-08-16T00:07:11.684266Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T00:07:11.670516Z","title":"Identity mappings in deep residual networks,","venue":null,"work_id":"4b536ffe-2b2f-4853-99fa-c4c08d1a6507","year":2016},"citing_paper":{"arxiv_id":"2505.03021","last_updated":"2025-05-05T20:44:42Z","snapshot_observed_at":"2026-08-15T23:59:32.131198Z","submitted_at":"2025-05-05T20:44:42Z","title":"A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-16T00:07:11.550317Z"},"links":{"citing_paper":"/paper/2505.03021"},"observation_digest":"sha256:660083bcca8f54f0a0d7fcbd2207b1ce4ddc04047b8f8b233b2d671f1465ac8b","observation_id":"82bb66a1-65d8-4a02-afb3-bd6095c43ed9","resolution":{"observed_at":"2026-08-16T00:07:11.674688Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T00:07:11.658211Z","title":"Delving deep into rectifiers: Surpassing human -level performance on imagenet classification,","venue":null,"work_id":"f3818a48-f6fa-4779-a05b-ae586daca51b","year":2015},"citing_paper":{"arxiv_id":"2505.03021","last_updated":"2025-05-05T20:44:42Z","snapshot_observed_at":"2026-08-15T23:59:32.131198Z","submitted_at":"2025-05-05T20:44:42Z","title":"A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-16T00:07:11.553912Z"},"links":{"citing_paper":"/paper/2505.03021"},"observation_digest":"sha256:5cfa824184d839dbf93fe6646bb8d206690497fb8c3e2205e7f1f461910d1b72","observation_id":"8ec39d4a-ca43-459b-905f-09beef881117","resolution":{"observed_at":"2026-08-16T00:07:11.662456Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T00:07:11.648112Z","title":"Reference constitutive model for Alloy 617 and 316H stainless steel for use with the ASME Division 5 design by inelastic analysis rules,","venue":null,"work_id":"dec5604d-6ce5-4717-bc9a-237c179e223c","year":2021},"citing_paper":{"arxiv_id":"2505.03021","last_updated":"2025-05-05T20:44:42Z","snapshot_observed_at":"2026-08-15T23:59:32.131198Z","submitted_at":"2025-05-05T20:44:42Z","title":"A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-16T00:07:11.557863Z"},"links":{"citing_paper":"/paper/2505.03021"},"observation_digest":"sha256:d51cab38ad2f0e6e94ae647ef37ea443c154dc4722ea092cbb3444399844264c","observation_id":"304bb0d8-094c-4605-bfad-375a6a45c120","resolution":{"observed_at":"2026-08-16T00:07:11.651515Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1606.08415","last_updated":"2023-06-06T01:53:32Z","snapshot_observed_at":"2026-08-13T19:48:28.322536Z","submitted_at":"2016-06-27T19:20:40Z","title":"Gaussian Error Linear Units (GELUs)","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1606.08415","snapshot_observed_at":"2026-08-16T00:07:11.561475Z","title":"Gaussian error linear units (gelus),","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2505.03021","last_updated":"2025-05-05T20:44:42Z","snapshot_observed_at":"2026-08-15T23:59:32.131198Z","submitted_at":"2025-05-05T20:44:42Z","title":"A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-16T00:07:11.561475Z"},"links":{"cited_paper":"/paper/1606.08415","citing_paper":"/paper/2505.03021"},"observation_digest":"sha256:8e3d02ac616906922434099fee60a03a9b9a20cf7272484ead1986813d420ef0","observation_id":"92bde4ab-831f-4a16-bf0d-96b6ebebd317","resolution":{"observed_at":"2026-08-16T00:07:11.561475Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-08-17T19:26:44.032537Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-16T00:07:11.564846Z","title":"Adam: A method for stochastic optimization,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2505.03021","last_updated":"2025-05-05T20:44:42Z","snapshot_observed_at":"2026-08-15T23:59:32.131198Z","submitted_at":"2025-05-05T20:44:42Z","title":"A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-16T00:07:11.564846Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2505.03021"},"observation_digest":"sha256:0a00f1150885bed69a45ca68daf1fd34ad670889700b2cb0884f388e8d7581d8","observation_id":"2a5571c8-deb9-4fda-9788-37fdfd8e19c0","resolution":{"observed_at":"2026-08-16T00:07:11.564846Z","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":"2026-08-16T00:07:11.634900Z","title":"Discovering governing equations from data by sparse identification of nonlinear dynamical systems,","venue":null,"work_id":"29005945-01a5-4636-a62e-d28555e215cc","year":2016},"citing_paper":{"arxiv_id":"2505.03021","last_updated":"2025-05-05T20:44:42Z","snapshot_observed_at":"2026-08-15T23:59:32.131198Z","submitted_at":"2025-05-05T20:44:42Z","title":"A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-16T00:07:11.568026Z"},"links":{"citing_paper":"/paper/2505.03021"},"observation_digest":"sha256:93bfde08e13be801594b604b32a6e8bea6ae3d71e7e7ba259cabbbe212f3282b","observation_id":"9dcae3af-34a1-47bb-8987-6831f14c7675","resolution":{"observed_at":"2026-08-16T00:07:11.640800Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2004.08424","last_updated":"2020-04-17T19:13:14Z","snapshot_observed_at":"2026-08-15T14:11:05.320551Z","submitted_at":"2020-04-17T19:13:14Z","title":"PySINDy: A Python package for the Sparse Identification of Nonlinear Dynamics from Data","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2004.08424","snapshot_observed_at":"2026-08-16T00:07:11.571870Z","title":"Pysindy: a python package for the sparse identification of nonlinear dynamics from data,","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2505.03021","last_updated":"2025-05-05T20:44:42Z","snapshot_observed_at":"2026-08-15T23:59:32.131198Z","submitted_at":"2025-05-05T20:44:42Z","title":"A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-16T00:07:11.571870Z"},"links":{"cited_paper":"/paper/2004.08424","citing_paper":"/paper/2505.03021"},"observation_digest":"sha256:6de83cee49eb58104f773d0020580bace0137dda178e20c20c72f3a7e241cb4c","observation_id":"2a3156f2-b3d0-4876-9259-6b4e35ea76b5","resolution":{"observed_at":"2026-08-16T00:07:11.571870Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.03021","last_updated":"2025-05-05T20:44:42Z","latest_version":1,"primary_category":"physics.comp-ph","snapshot_observed_at":"2026-08-15T23:59:32.131198Z","submitted_at":"2025-05-05T20:44:42Z","title":"A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEs"},"reference_resolution":{"displayed":32,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":5,"verified_exact":0,"verified_fuzzy":27},"total_outbound_references":32},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2505.03021."}