{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:IXLFUZVROYR72B7DG2XVO6VKZF","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"c4f354def5dd7bc1655e09340086bb591c5f3d669110d3c8561909878bb27c24","cross_cats_sorted":["cs.CE","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-02-14T08:24:53Z","title_canon_sha256":"d1ced76e28f616cec7e9a9cf823eae2936a010278741196918a78b02a9dee107"},"schema_version":"1.0","source":{"id":"2003.02751","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2003.02751","created_at":"2026-07-05T01:01:56Z"},{"alias_kind":"arxiv_version","alias_value":"2003.02751v2","created_at":"2026-07-05T01:01:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2003.02751","created_at":"2026-07-05T01:01:56Z"},{"alias_kind":"pith_short_12","alias_value":"IXLFUZVROYR7","created_at":"2026-07-05T01:01:56Z"},{"alias_kind":"pith_short_16","alias_value":"IXLFUZVROYR72B7D","created_at":"2026-07-05T01:01:56Z"},{"alias_kind":"pith_short_8","alias_value":"IXLFUZVR","created_at":"2026-07-05T01:01:56Z"}],"graph_snapshots":[{"event_id":"sha256:7ac5ec7baa18c16b0f4984d672f0f108d688fcf37742ce0ca0c5b9df502c16e8","target":"graph","created_at":"2026-07-05T01:01:56Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2003.02751/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We present the application of a class of deep learning, known as Physics Informed Neural Networks (PINN), to learning and discovery in solid mechanics. We explain how to incorporate the momentum balance and constitutive relations into PINN, and explore in detail the application to linear elasticity, and illustrate its extension to nonlinear problems through an example that showcases von~Mises elastoplasticity. While common PINN algorithms are based on training one deep neural network (DNN), we propose a multi-network model that results in more accurate representation of the field variables. To","authors_text":"Adrian Moure, Ehsan Haghighat, Hector Gomez, Maziar Raissi, Ruben Juanes","cross_cats":["cs.CE","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-02-14T08:24:53Z","title":"A deep learning framework for solution and discovery in solid mechanics"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2003.02751","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:8403a2382304f88867bbe06f3694c7883896832156c38bfa591a2102efdb0d60","target":"record","created_at":"2026-07-05T01:01:56Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"c4f354def5dd7bc1655e09340086bb591c5f3d669110d3c8561909878bb27c24","cross_cats_sorted":["cs.CE","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-02-14T08:24:53Z","title_canon_sha256":"d1ced76e28f616cec7e9a9cf823eae2936a010278741196918a78b02a9dee107"},"schema_version":"1.0","source":{"id":"2003.02751","kind":"arxiv","version":2}},"canonical_sha256":"45d65a66b17623fd07e336af577aaac94265bd45041e6576991995227d1f5c39","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"45d65a66b17623fd07e336af577aaac94265bd45041e6576991995227d1f5c39","first_computed_at":"2026-07-05T01:01:56.845086Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:01:56.845086Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"5lJR+nwTH7tw/aJUaPzirpHHmliDivwK9dxsdnB7/0FRJiXaaFAWJnRHTtCDuVZxmaxcKihMOLYaofPQRJ+4BA==","signature_status":"signed_v1","signed_at":"2026-07-05T01:01:56.845496Z","signed_message":"canonical_sha256_bytes"},"source_id":"2003.02751","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8403a2382304f88867bbe06f3694c7883896832156c38bfa591a2102efdb0d60","sha256:7ac5ec7baa18c16b0f4984d672f0f108d688fcf37742ce0ca0c5b9df502c16e8"],"state_sha256":"a6fcd6dea3c84410b870e93685b268814ec981c67cafceac34ff17c64c19c50c"}