{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:IYCDRRNPO7FDJD24NNUXG4QTIX","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":"2c62bc5532ce390ad0f9e5dc634626acf504a420235f9389b6cee56fd76e52d1","cross_cats_sorted":["cs.LG","cs.NA"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.NA","submitted_at":"2025-06-21T14:51:46Z","title_canon_sha256":"d3ce0d874494637d257ed2b4c8034bb030f333d6e97a2683d48fb7e6a0b71ae3"},"schema_version":"1.0","source":{"id":"2506.17726","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.17726","created_at":"2026-07-05T11:25:22Z"},{"alias_kind":"arxiv_version","alias_value":"2506.17726v1","created_at":"2026-07-05T11:25:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.17726","created_at":"2026-07-05T11:25:22Z"},{"alias_kind":"pith_short_12","alias_value":"IYCDRRNPO7FD","created_at":"2026-07-05T11:25:22Z"},{"alias_kind":"pith_short_16","alias_value":"IYCDRRNPO7FDJD24","created_at":"2026-07-05T11:25:22Z"},{"alias_kind":"pith_short_8","alias_value":"IYCDRRNP","created_at":"2026-07-05T11:25:22Z"}],"graph_snapshots":[{"event_id":"sha256:6eb94d9915e6fe08d6f7f5e54c08d8fc6a31fe7718f4918212701a79d1eadf26","target":"graph","created_at":"2026-07-05T11:25:22Z","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/2506.17726/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this paper, the physics informed neural networks (PINNs) is employed for the numerical simulation of heat transfer involving a moving source. To reduce the computational effort, a new training method is proposed that uses a continuous time-stepping through transfer learning. Within this, the time interval is divided into smaller intervals and a single network is initialized. On this single network each time interval is trained with the initial condition for (n+1)th as the solution obtained at nth time increment. Thus, this framework enables the computation of large temporal intervals withou","authors_text":"Anirudh Kalyan, Sundararajan Natarajan","cross_cats":["cs.LG","cs.NA"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.NA","submitted_at":"2025-06-21T14:51:46Z","title":"Numerical simulation of transient heat conduction with moving heat source using Physics Informed Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.17726","kind":"arxiv","version":1},"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:abd15b7e8b31938c3b2c1d1b019b1173da61af8fe6d4e22e008f90820b275a1c","target":"record","created_at":"2026-07-05T11:25:22Z","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":"2c62bc5532ce390ad0f9e5dc634626acf504a420235f9389b6cee56fd76e52d1","cross_cats_sorted":["cs.LG","cs.NA"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.NA","submitted_at":"2025-06-21T14:51:46Z","title_canon_sha256":"d3ce0d874494637d257ed2b4c8034bb030f333d6e97a2683d48fb7e6a0b71ae3"},"schema_version":"1.0","source":{"id":"2506.17726","kind":"arxiv","version":1}},"canonical_sha256":"460438c5af77ca348f5c6b6973721345e49fddb4eafaffd61ad5c9ee6856536e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"460438c5af77ca348f5c6b6973721345e49fddb4eafaffd61ad5c9ee6856536e","first_computed_at":"2026-07-05T11:25:22.416969Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:25:22.416969Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"azdKuWFjObmsYbAg5KBY7X+aRSiiKW0wvHgKMIp0FQnXg2QZBXW73e2gmIUFGOtVJ3pk/861ZshozzRGaqPYAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:25:22.417348Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.17726","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:abd15b7e8b31938c3b2c1d1b019b1173da61af8fe6d4e22e008f90820b275a1c","sha256:6eb94d9915e6fe08d6f7f5e54c08d8fc6a31fe7718f4918212701a79d1eadf26"],"state_sha256":"e8ca76ab31fc1d9ab61957fca1d8355a5759cbc2a2dbe03c90761949a2ef8226"}