{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:MYKLYDSL4KJ455VCS5RNOCDMUM","short_pith_number":"pith:MYKLYDSL","schema_version":"1.0","canonical_sha256":"6614bc0e4be293cef6a29762d7086ca327f877b27b517f910d68bc53bc44f63d","source":{"kind":"arxiv","id":"2401.02403","version":1},"attestation_state":"computed","paper":{"title":"Real-Time 2D Temperature Field Prediction in Metal Additive Manufacturing Using Physics-Informed Neural Networks","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"G. Gary Wang, Mostafa Rahmani Dehaghani, Pouyan Sajadi, Yifan Tang","submitted_at":"2024-01-04T18:42:28Z","abstract_excerpt":"Accurately predicting the temperature field in metal additive manufacturing (AM) processes is critical to preventing overheating, adjusting process parameters, and ensuring process stability. While physics-based computational models offer precision, they are often time-consuming and unsuitable for real-time predictions and online control in iterative design scenarios. Conversely, machine learning models rely heavily on high-quality datasets, which can be costly and challenging to obtain within the metal AM domain. Our work addresses this by introducing a physics-informed neural network framewo"},"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":"2401.02403","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-01-04T18:42:28Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"57762f9bb6ec63215f3618470f6655db7aa2ec6a7a93fe20f2d4ad3bf26e5977","abstract_canon_sha256":"dbdf7eae99468093b13e0317a13e3937a90c02524dbc20d0e9da4dcd9a5d52c9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:30:16.184132Z","signature_b64":"cKxnT71QFLEjpeWQo2INxaPy0IEie7LUE2LMPeclAJM9pzGcVXiQbJALLFUC1YSLmPXCdOy/oikKtQd9PJTsCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6614bc0e4be293cef6a29762d7086ca327f877b27b517f910d68bc53bc44f63d","last_reissued_at":"2026-07-05T07:30:16.183731Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:30:16.183731Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Real-Time 2D Temperature Field Prediction in Metal Additive Manufacturing Using Physics-Informed Neural Networks","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"G. Gary Wang, Mostafa Rahmani Dehaghani, Pouyan Sajadi, Yifan Tang","submitted_at":"2024-01-04T18:42:28Z","abstract_excerpt":"Accurately predicting the temperature field in metal additive manufacturing (AM) processes is critical to preventing overheating, adjusting process parameters, and ensuring process stability. While physics-based computational models offer precision, they are often time-consuming and unsuitable for real-time predictions and online control in iterative design scenarios. Conversely, machine learning models rely heavily on high-quality datasets, which can be costly and challenging to obtain within the metal AM domain. Our work addresses this by introducing a physics-informed neural network framewo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.02403","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/2401.02403/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":"2401.02403","created_at":"2026-07-05T07:30:16.183788+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.02403v1","created_at":"2026-07-05T07:30:16.183788+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.02403","created_at":"2026-07-05T07:30:16.183788+00:00"},{"alias_kind":"pith_short_12","alias_value":"MYKLYDSL4KJ4","created_at":"2026-07-05T07:30:16.183788+00:00"},{"alias_kind":"pith_short_16","alias_value":"MYKLYDSL4KJ455VC","created_at":"2026-07-05T07:30:16.183788+00:00"},{"alias_kind":"pith_short_8","alias_value":"MYKLYDSL","created_at":"2026-07-05T07:30:16.183788+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.02712","citing_title":"Physics-guided denoiser network for enhanced additive manufacturing data quality","ref_index":1,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MYKLYDSL4KJ455VCS5RNOCDMUM","json":"https://pith.science/pith/MYKLYDSL4KJ455VCS5RNOCDMUM.json","graph_json":"https://pith.science/api/pith-number/MYKLYDSL4KJ455VCS5RNOCDMUM/graph.json","events_json":"https://pith.science/api/pith-number/MYKLYDSL4KJ455VCS5RNOCDMUM/events.json","paper":"https://pith.science/paper/MYKLYDSL"},"agent_actions":{"view_html":"https://pith.science/pith/MYKLYDSL4KJ455VCS5RNOCDMUM","download_json":"https://pith.science/pith/MYKLYDSL4KJ455VCS5RNOCDMUM.json","view_paper":"https://pith.science/paper/MYKLYDSL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.02403&json=true","fetch_graph":"https://pith.science/api/pith-number/MYKLYDSL4KJ455VCS5RNOCDMUM/graph.json","fetch_events":"https://pith.science/api/pith-number/MYKLYDSL4KJ455VCS5RNOCDMUM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MYKLYDSL4KJ455VCS5RNOCDMUM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MYKLYDSL4KJ455VCS5RNOCDMUM/action/storage_attestation","attest_author":"https://pith.science/pith/MYKLYDSL4KJ455VCS5RNOCDMUM/action/author_attestation","sign_citation":"https://pith.science/pith/MYKLYDSL4KJ455VCS5RNOCDMUM/action/citation_signature","submit_replication":"https://pith.science/pith/MYKLYDSL4KJ455VCS5RNOCDMUM/action/replication_record"}},"created_at":"2026-07-05T07:30:16.183788+00:00","updated_at":"2026-07-05T07:30:16.183788+00:00"}