{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:2L2RJHEEFXWEHYJVMQALIRFXWD","short_pith_number":"pith:2L2RJHEE","schema_version":"1.0","canonical_sha256":"d2f5149c842dec43e1356400b444b7b0e1b8e9b9f18eabf3040ed36a87a7b07d","source":{"kind":"arxiv","id":"2304.03552","version":2},"attestation_state":"computed","paper":{"title":"A physics-informed neural network framework for modeling obstacle-related equations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","cs.NA","math.AP","math.IT","math.NA"],"primary_cat":"cs.LG","authors_text":"Bubacarr Bah, Hamid El Bahja, Issa Karambal, Jan Christian Hauffen, Peter Jung","submitted_at":"2023-04-07T09:22:28Z","abstract_excerpt":"Deep learning has been highly successful in some applications. Nevertheless, its use for solving partial differential equations (PDEs) has only been of recent interest with current state-of-the-art machine learning libraries, e.g., TensorFlow or PyTorch. Physics-informed neural networks (PINNs) are an attractive tool for solving partial differential equations based on sparse and noisy data. Here extend PINNs to solve obstacle-related PDEs which present a great computational challenge because they necessitate numerical methods that can yield an accurate approximation of the solution that lies a"},"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":"2304.03552","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-04-07T09:22:28Z","cross_cats_sorted":["cs.IT","cs.NA","math.AP","math.IT","math.NA"],"title_canon_sha256":"c5f5e0f344723d6e389da6e0744f68558985030b12a021ed8435da64f445246a","abstract_canon_sha256":"c9a24047939a417d386d2a2de23bb6ee818dbd26296803f2896435c764df85bf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:22:58.869269Z","signature_b64":"vI9tc+laVjN1qFQkp1B2Byfzq6NwbtpOw3TuBBDQPPraGp+iF1owokG5SvhRiJ0L7BtWqLz4Aoax3XjAk5vdAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d2f5149c842dec43e1356400b444b7b0e1b8e9b9f18eabf3040ed36a87a7b07d","last_reissued_at":"2026-07-05T09:22:58.868880Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:22:58.868880Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A physics-informed neural network framework for modeling obstacle-related equations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","cs.NA","math.AP","math.IT","math.NA"],"primary_cat":"cs.LG","authors_text":"Bubacarr Bah, Hamid El Bahja, Issa Karambal, Jan Christian Hauffen, Peter Jung","submitted_at":"2023-04-07T09:22:28Z","abstract_excerpt":"Deep learning has been highly successful in some applications. Nevertheless, its use for solving partial differential equations (PDEs) has only been of recent interest with current state-of-the-art machine learning libraries, e.g., TensorFlow or PyTorch. Physics-informed neural networks (PINNs) are an attractive tool for solving partial differential equations based on sparse and noisy data. Here extend PINNs to solve obstacle-related PDEs which present a great computational challenge because they necessitate numerical methods that can yield an accurate approximation of the solution that lies a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2304.03552","kind":"arxiv","version":2},"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/2304.03552/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":"2304.03552","created_at":"2026-07-05T09:22:58.868936+00:00"},{"alias_kind":"arxiv_version","alias_value":"2304.03552v2","created_at":"2026-07-05T09:22:58.868936+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.03552","created_at":"2026-07-05T09:22:58.868936+00:00"},{"alias_kind":"pith_short_12","alias_value":"2L2RJHEEFXWE","created_at":"2026-07-05T09:22:58.868936+00:00"},{"alias_kind":"pith_short_16","alias_value":"2L2RJHEEFXWEHYJV","created_at":"2026-07-05T09:22:58.868936+00:00"},{"alias_kind":"pith_short_8","alias_value":"2L2RJHEE","created_at":"2026-07-05T09:22:58.868936+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2L2RJHEEFXWEHYJVMQALIRFXWD","json":"https://pith.science/pith/2L2RJHEEFXWEHYJVMQALIRFXWD.json","graph_json":"https://pith.science/api/pith-number/2L2RJHEEFXWEHYJVMQALIRFXWD/graph.json","events_json":"https://pith.science/api/pith-number/2L2RJHEEFXWEHYJVMQALIRFXWD/events.json","paper":"https://pith.science/paper/2L2RJHEE"},"agent_actions":{"view_html":"https://pith.science/pith/2L2RJHEEFXWEHYJVMQALIRFXWD","download_json":"https://pith.science/pith/2L2RJHEEFXWEHYJVMQALIRFXWD.json","view_paper":"https://pith.science/paper/2L2RJHEE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2304.03552&json=true","fetch_graph":"https://pith.science/api/pith-number/2L2RJHEEFXWEHYJVMQALIRFXWD/graph.json","fetch_events":"https://pith.science/api/pith-number/2L2RJHEEFXWEHYJVMQALIRFXWD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2L2RJHEEFXWEHYJVMQALIRFXWD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2L2RJHEEFXWEHYJVMQALIRFXWD/action/storage_attestation","attest_author":"https://pith.science/pith/2L2RJHEEFXWEHYJVMQALIRFXWD/action/author_attestation","sign_citation":"https://pith.science/pith/2L2RJHEEFXWEHYJVMQALIRFXWD/action/citation_signature","submit_replication":"https://pith.science/pith/2L2RJHEEFXWEHYJVMQALIRFXWD/action/replication_record"}},"created_at":"2026-07-05T09:22:58.868936+00:00","updated_at":"2026-07-05T09:22:58.868936+00:00"}