{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:YTV5ILDIUIDSNUDNPVNCDFXR4L","short_pith_number":"pith:YTV5ILDI","schema_version":"1.0","canonical_sha256":"c4ebd42c68a20726d06d7d5a2196f1e2edf50cb5939594e8bab9dd2efd8a4573","source":{"kind":"arxiv","id":"2508.01463","version":2},"attestation_state":"computed","paper":{"title":"Extended Interface Physics-Informed Neural Networks Method for Moving Interface Problems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NA"],"primary_cat":"math.NA","authors_text":"Ran Bi, Weibing Deng, Yameng Zhu","submitted_at":"2025-08-02T18:41:58Z","abstract_excerpt":"Physics-informed neural networks (PINNs) have emerged as an effective class of mesh-free methods for solving partial differential equations (PDEs), particularly on complex geometries. In this paper, we introduce an Extended Interface Physics-Informed Neural Network (XI-PINN) framework designed to solve parabolic moving interface problems. The proposed method employs a level set function--which can be either analytically prescribed or learned via a neural network--to capture the moving interface. Furthermore, we establish an a priori error analysis for the XI-PINN method and derive error bounds"},"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":"2508.01463","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2025-08-02T18:41:58Z","cross_cats_sorted":["cs.NA"],"title_canon_sha256":"abfcee9e56b2abdbcd780193c6ba870459f575e355422dadc4306d07a02ed65c","abstract_canon_sha256":"190508c3f1223c5c8ac560b7a3b07481a1bacb668ca3f1bbd3e1b47f351d6695"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-03T01:17:12.418535Z","signature_b64":"b6cIFmUss/lbHNT5D63A3Dzd/l49+OiH5+k8oK43udE53hU6jcLgwUYJbx+xGwNazlRp41Uwe1maS9u8q1OxCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c4ebd42c68a20726d06d7d5a2196f1e2edf50cb5939594e8bab9dd2efd8a4573","last_reissued_at":"2026-07-03T01:17:12.418059Z","signature_status":"signed_v1","first_computed_at":"2026-07-03T01:17:12.418059Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Extended Interface Physics-Informed Neural Networks Method for Moving Interface Problems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NA"],"primary_cat":"math.NA","authors_text":"Ran Bi, Weibing Deng, Yameng Zhu","submitted_at":"2025-08-02T18:41:58Z","abstract_excerpt":"Physics-informed neural networks (PINNs) have emerged as an effective class of mesh-free methods for solving partial differential equations (PDEs), particularly on complex geometries. In this paper, we introduce an Extended Interface Physics-Informed Neural Network (XI-PINN) framework designed to solve parabolic moving interface problems. The proposed method employs a level set function--which can be either analytically prescribed or learned via a neural network--to capture the moving interface. Furthermore, we establish an a priori error analysis for the XI-PINN method and derive error bounds"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.01463","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/2508.01463/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":"2508.01463","created_at":"2026-07-03T01:17:12.418120+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.01463v2","created_at":"2026-07-03T01:17:12.418120+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.01463","created_at":"2026-07-03T01:17:12.418120+00:00"},{"alias_kind":"pith_short_12","alias_value":"YTV5ILDIUIDS","created_at":"2026-07-03T01:17:12.418120+00:00"},{"alias_kind":"pith_short_16","alias_value":"YTV5ILDIUIDSNUDN","created_at":"2026-07-03T01:17:12.418120+00:00"},{"alias_kind":"pith_short_8","alias_value":"YTV5ILDI","created_at":"2026-07-03T01:17:12.418120+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2604.08869","citing_title":"Adaptive Randomized Neural Networks with Locally Activation Function: Theory and Algorithm for Solving PDEs","ref_index":3,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YTV5ILDIUIDSNUDNPVNCDFXR4L","json":"https://pith.science/pith/YTV5ILDIUIDSNUDNPVNCDFXR4L.json","graph_json":"https://pith.science/api/pith-number/YTV5ILDIUIDSNUDNPVNCDFXR4L/graph.json","events_json":"https://pith.science/api/pith-number/YTV5ILDIUIDSNUDNPVNCDFXR4L/events.json","paper":"https://pith.science/paper/YTV5ILDI"},"agent_actions":{"view_html":"https://pith.science/pith/YTV5ILDIUIDSNUDNPVNCDFXR4L","download_json":"https://pith.science/pith/YTV5ILDIUIDSNUDNPVNCDFXR4L.json","view_paper":"https://pith.science/paper/YTV5ILDI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.01463&json=true","fetch_graph":"https://pith.science/api/pith-number/YTV5ILDIUIDSNUDNPVNCDFXR4L/graph.json","fetch_events":"https://pith.science/api/pith-number/YTV5ILDIUIDSNUDNPVNCDFXR4L/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YTV5ILDIUIDSNUDNPVNCDFXR4L/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YTV5ILDIUIDSNUDNPVNCDFXR4L/action/storage_attestation","attest_author":"https://pith.science/pith/YTV5ILDIUIDSNUDNPVNCDFXR4L/action/author_attestation","sign_citation":"https://pith.science/pith/YTV5ILDIUIDSNUDNPVNCDFXR4L/action/citation_signature","submit_replication":"https://pith.science/pith/YTV5ILDIUIDSNUDNPVNCDFXR4L/action/replication_record"}},"created_at":"2026-07-03T01:17:12.418120+00:00","updated_at":"2026-07-03T01:17:12.418120+00:00"}