{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:CMZ7LJ57P5DOO6DEFEY25ZU6GE","short_pith_number":"pith:CMZ7LJ57","schema_version":"1.0","canonical_sha256":"1333f5a7bf7f46e778642931aee69e3109c46944ad5f99de6b8df276a0bfe7e2","source":{"kind":"arxiv","id":"2505.20361","version":1},"attestation_state":"computed","paper":{"title":"Solving Euler equations with Multiple Discontinuities via Separation-Transfer Physics-Informed Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"physics.flu-dyn","authors_text":"Chuanxing Wang, Guohuai Zhu, Hui Luo, Kai Wang, Mingxing Luo","submitted_at":"2025-05-26T08:55:04Z","abstract_excerpt":"Despite the remarkable progress of physics-informed neural networks (PINNs) in scientific computing, they continue to face challenges when solving hydrodynamic problems with multiple discontinuities. In this work, we propose Separation-Transfer Physics Informed Neural Networks (ST-PINNs) to address such problems. By sequentially resolving discontinuities from strong to weak and leveraging transfer learning during training, ST-PINNs significantly reduce the problem complexity and enhance solution accuracy. To the best of our knowledge, this is the first study to apply a PINNs-based approach to "},"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":"2505.20361","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.flu-dyn","submitted_at":"2025-05-26T08:55:04Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"de44828113687d13ca02ba5f51fa02d627698ce519df9e732ecc4e199a857e45","abstract_canon_sha256":"285d91020afc5a342a9f9e0587817680a59b6a996b1ac68f3d13e1204150ab8f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:10:11.765573Z","signature_b64":"hLdEcN6rEwWlHdeO1K87RMiIt/ZP+7x4UK9nvWLAlEukLvpz8oqPK6m4dbu+yp+6XNK9GDV9ukheOe8IcHQTBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1333f5a7bf7f46e778642931aee69e3109c46944ad5f99de6b8df276a0bfe7e2","last_reissued_at":"2026-07-05T11:10:11.765067Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:10:11.765067Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Solving Euler equations with Multiple Discontinuities via Separation-Transfer Physics-Informed Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"physics.flu-dyn","authors_text":"Chuanxing Wang, Guohuai Zhu, Hui Luo, Kai Wang, Mingxing Luo","submitted_at":"2025-05-26T08:55:04Z","abstract_excerpt":"Despite the remarkable progress of physics-informed neural networks (PINNs) in scientific computing, they continue to face challenges when solving hydrodynamic problems with multiple discontinuities. In this work, we propose Separation-Transfer Physics Informed Neural Networks (ST-PINNs) to address such problems. By sequentially resolving discontinuities from strong to weak and leveraging transfer learning during training, ST-PINNs significantly reduce the problem complexity and enhance solution accuracy. To the best of our knowledge, this is the first study to apply a PINNs-based approach to "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.20361","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/2505.20361/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":"2505.20361","created_at":"2026-07-05T11:10:11.765121+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.20361v1","created_at":"2026-07-05T11:10:11.765121+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.20361","created_at":"2026-07-05T11:10:11.765121+00:00"},{"alias_kind":"pith_short_12","alias_value":"CMZ7LJ57P5DO","created_at":"2026-07-05T11:10:11.765121+00:00"},{"alias_kind":"pith_short_16","alias_value":"CMZ7LJ57P5DOO6DE","created_at":"2026-07-05T11:10:11.765121+00:00"},{"alias_kind":"pith_short_8","alias_value":"CMZ7LJ57","created_at":"2026-07-05T11:10:11.765121+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2608.10389","citing_title":"Efficient Weak-Entropy PINN for Solving Hyperbolic Conservation Laws","ref_index":28,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CMZ7LJ57P5DOO6DEFEY25ZU6GE","json":"https://pith.science/pith/CMZ7LJ57P5DOO6DEFEY25ZU6GE.json","graph_json":"https://pith.science/api/pith-number/CMZ7LJ57P5DOO6DEFEY25ZU6GE/graph.json","events_json":"https://pith.science/api/pith-number/CMZ7LJ57P5DOO6DEFEY25ZU6GE/events.json","paper":"https://pith.science/paper/CMZ7LJ57"},"agent_actions":{"view_html":"https://pith.science/pith/CMZ7LJ57P5DOO6DEFEY25ZU6GE","download_json":"https://pith.science/pith/CMZ7LJ57P5DOO6DEFEY25ZU6GE.json","view_paper":"https://pith.science/paper/CMZ7LJ57","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.20361&json=true","fetch_graph":"https://pith.science/api/pith-number/CMZ7LJ57P5DOO6DEFEY25ZU6GE/graph.json","fetch_events":"https://pith.science/api/pith-number/CMZ7LJ57P5DOO6DEFEY25ZU6GE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CMZ7LJ57P5DOO6DEFEY25ZU6GE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CMZ7LJ57P5DOO6DEFEY25ZU6GE/action/storage_attestation","attest_author":"https://pith.science/pith/CMZ7LJ57P5DOO6DEFEY25ZU6GE/action/author_attestation","sign_citation":"https://pith.science/pith/CMZ7LJ57P5DOO6DEFEY25ZU6GE/action/citation_signature","submit_replication":"https://pith.science/pith/CMZ7LJ57P5DOO6DEFEY25ZU6GE/action/replication_record"}},"created_at":"2026-07-05T11:10:11.765121+00:00","updated_at":"2026-07-05T11:10:11.765121+00:00"}