{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:HNZGGS74WULKRYD5N3USGEGE57","short_pith_number":"pith:HNZGGS74","schema_version":"1.0","canonical_sha256":"3b72634bfcb516a8e07d6ee92310c4eff5686aed0626fc75d21d7abc9af48a77","source":{"kind":"arxiv","id":"2006.08472","version":1},"attestation_state":"computed","paper":{"title":"Physics informed deep learning for computational elastodynamics without labeled data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CE","cs.LG","cs.NA"],"primary_cat":"math.NA","authors_text":"Chengping Rao, Hao Sun, Yang Liu","submitted_at":"2020-06-10T19:05:08Z","abstract_excerpt":"Numerical methods such as finite element have been flourishing in the past decades for modeling solid mechanics problems via solving governing partial differential equations (PDEs). A salient aspect that distinguishes these numerical methods is how they approximate the physical fields of interest. Physics-informed deep learning is a novel approach recently developed for modeling PDE solutions and shows promise to solve computational mechanics problems without using any labeled data. The philosophy behind it is to approximate the quantity of interest (e.g., PDE solution variables) by a deep neu"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2006.08472","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2020-06-10T19:05:08Z","cross_cats_sorted":["cs.AI","cs.CE","cs.LG","cs.NA"],"title_canon_sha256":"e87945b2fe5db5916583f54655a1e7f51f08c0f227912645648076c17dd0d9bc","abstract_canon_sha256":"af7d5e2ed6ca743980e5700b5a97cb47ed1c8c437a3854f88e7dba24d00fe233"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:10:21.931757Z","signature_b64":"iVTk6AvxZrTp5ZuF1w3o1oMbUBNPw/3kH9YEEsQYS1S+DTCfrCg+i1DrscIKuxRA1cfDTxtW9CAufDfTYXm9CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3b72634bfcb516a8e07d6ee92310c4eff5686aed0626fc75d21d7abc9af48a77","last_reissued_at":"2026-07-05T01:10:21.931340Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:10:21.931340Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Physics informed deep learning for computational elastodynamics without labeled data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CE","cs.LG","cs.NA"],"primary_cat":"math.NA","authors_text":"Chengping Rao, Hao Sun, Yang Liu","submitted_at":"2020-06-10T19:05:08Z","abstract_excerpt":"Numerical methods such as finite element have been flourishing in the past decades for modeling solid mechanics problems via solving governing partial differential equations (PDEs). A salient aspect that distinguishes these numerical methods is how they approximate the physical fields of interest. Physics-informed deep learning is a novel approach recently developed for modeling PDE solutions and shows promise to solve computational mechanics problems without using any labeled data. The philosophy behind it is to approximate the quantity of interest (e.g., PDE solution variables) by a deep neu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.08472","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2006.08472/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2006.08472","created_at":"2026-07-05T01:10:21.931398+00:00"},{"alias_kind":"arxiv_version","alias_value":"2006.08472v1","created_at":"2026-07-05T01:10:21.931398+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.08472","created_at":"2026-07-05T01:10:21.931398+00:00"},{"alias_kind":"pith_short_12","alias_value":"HNZGGS74WULK","created_at":"2026-07-05T01:10:21.931398+00:00"},{"alias_kind":"pith_short_16","alias_value":"HNZGGS74WULKRYD5","created_at":"2026-07-05T01:10:21.931398+00:00"},{"alias_kind":"pith_short_8","alias_value":"HNZGGS74","created_at":"2026-07-05T01:10:21.931398+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.04263","citing_title":"Physics-Informed DeepONets for drift-diffusion on metric graphs: simulation and parameter identification","ref_index":15,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HNZGGS74WULKRYD5N3USGEGE57","json":"https://pith.science/pith/HNZGGS74WULKRYD5N3USGEGE57.json","graph_json":"https://pith.science/api/pith-number/HNZGGS74WULKRYD5N3USGEGE57/graph.json","events_json":"https://pith.science/api/pith-number/HNZGGS74WULKRYD5N3USGEGE57/events.json","paper":"https://pith.science/paper/HNZGGS74"},"agent_actions":{"view_html":"https://pith.science/pith/HNZGGS74WULKRYD5N3USGEGE57","download_json":"https://pith.science/pith/HNZGGS74WULKRYD5N3USGEGE57.json","view_paper":"https://pith.science/paper/HNZGGS74","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2006.08472&json=true","fetch_graph":"https://pith.science/api/pith-number/HNZGGS74WULKRYD5N3USGEGE57/graph.json","fetch_events":"https://pith.science/api/pith-number/HNZGGS74WULKRYD5N3USGEGE57/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HNZGGS74WULKRYD5N3USGEGE57/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HNZGGS74WULKRYD5N3USGEGE57/action/storage_attestation","attest_author":"https://pith.science/pith/HNZGGS74WULKRYD5N3USGEGE57/action/author_attestation","sign_citation":"https://pith.science/pith/HNZGGS74WULKRYD5N3USGEGE57/action/citation_signature","submit_replication":"https://pith.science/pith/HNZGGS74WULKRYD5N3USGEGE57/action/replication_record"}},"created_at":"2026-07-05T01:10:21.931398+00:00","updated_at":"2026-07-05T01:10:21.931398+00:00"}