{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:2I7XDYKKU5LBB67ZJILI3INE5H","short_pith_number":"pith:2I7XDYKK","schema_version":"1.0","canonical_sha256":"d23f71e14aa75610fbf94a168da1a4e9d391ca31d531fd9e12aeb7eb9a6d278b","source":{"kind":"arxiv","id":"2401.11860","version":1},"attestation_state":"computed","paper":{"title":"A Review of Physics-Informed Machine Learning Methods with Applications to Condition Monitoring and Anomaly Detection","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI","cs.SY","eess.SY"],"primary_cat":"cs.LG","authors_text":"Brett Sicard, Stephen Andrew Gadsden, Yuandi Wu","submitted_at":"2024-01-22T11:29:44Z","abstract_excerpt":"This study presents a comprehensive overview of PIML techniques in the context of condition monitoring. The central concept driving PIML is the incorporation of known physical laws and constraints into machine learning algorithms, enabling them to learn from available data while remaining consistent with physical principles. Through fusing domain knowledge with data-driven learning, PIML methods offer enhanced accuracy and interpretability in comparison to purely data-driven approaches. In this comprehensive survey, detailed examinations are performed with regard to the methodology by which kn"},"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":"2401.11860","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2024-01-22T11:29:44Z","cross_cats_sorted":["cs.AI","cs.SY","eess.SY"],"title_canon_sha256":"ea9342669860583657129524fd888eeec42beebea83598c907efd862bd615f47","abstract_canon_sha256":"71ee41e36fcb40d79d2114223a923002a58e2da04b31774620bc96103a6f6f30"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:36:11.214813Z","signature_b64":"4aEzY81UPmmHi0cX6yGwnIVNeTAx234ERO4gMXqdKU+emNwvJ5BAzEmupVQx8gwGmF9xvncaQTm7LpkdkVyKBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d23f71e14aa75610fbf94a168da1a4e9d391ca31d531fd9e12aeb7eb9a6d278b","last_reissued_at":"2026-07-05T07:36:11.214359Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:36:11.214359Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Review of Physics-Informed Machine Learning Methods with Applications to Condition Monitoring and Anomaly Detection","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI","cs.SY","eess.SY"],"primary_cat":"cs.LG","authors_text":"Brett Sicard, Stephen Andrew Gadsden, Yuandi Wu","submitted_at":"2024-01-22T11:29:44Z","abstract_excerpt":"This study presents a comprehensive overview of PIML techniques in the context of condition monitoring. The central concept driving PIML is the incorporation of known physical laws and constraints into machine learning algorithms, enabling them to learn from available data while remaining consistent with physical principles. Through fusing domain knowledge with data-driven learning, PIML methods offer enhanced accuracy and interpretability in comparison to purely data-driven approaches. In this comprehensive survey, detailed examinations are performed with regard to the methodology by which kn"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.11860","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/2401.11860/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":"2401.11860","created_at":"2026-07-05T07:36:11.214428+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.11860v1","created_at":"2026-07-05T07:36:11.214428+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.11860","created_at":"2026-07-05T07:36:11.214428+00:00"},{"alias_kind":"pith_short_12","alias_value":"2I7XDYKKU5LB","created_at":"2026-07-05T07:36:11.214428+00:00"},{"alias_kind":"pith_short_16","alias_value":"2I7XDYKKU5LBB67Z","created_at":"2026-07-05T07:36:11.214428+00:00"},{"alias_kind":"pith_short_8","alias_value":"2I7XDYKK","created_at":"2026-07-05T07:36:11.214428+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.20649","citing_title":"Physics-Constrained Machine Learning for Chemical Engineering","ref_index":40,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2I7XDYKKU5LBB67ZJILI3INE5H","json":"https://pith.science/pith/2I7XDYKKU5LBB67ZJILI3INE5H.json","graph_json":"https://pith.science/api/pith-number/2I7XDYKKU5LBB67ZJILI3INE5H/graph.json","events_json":"https://pith.science/api/pith-number/2I7XDYKKU5LBB67ZJILI3INE5H/events.json","paper":"https://pith.science/paper/2I7XDYKK"},"agent_actions":{"view_html":"https://pith.science/pith/2I7XDYKKU5LBB67ZJILI3INE5H","download_json":"https://pith.science/pith/2I7XDYKKU5LBB67ZJILI3INE5H.json","view_paper":"https://pith.science/paper/2I7XDYKK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.11860&json=true","fetch_graph":"https://pith.science/api/pith-number/2I7XDYKKU5LBB67ZJILI3INE5H/graph.json","fetch_events":"https://pith.science/api/pith-number/2I7XDYKKU5LBB67ZJILI3INE5H/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2I7XDYKKU5LBB67ZJILI3INE5H/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2I7XDYKKU5LBB67ZJILI3INE5H/action/storage_attestation","attest_author":"https://pith.science/pith/2I7XDYKKU5LBB67ZJILI3INE5H/action/author_attestation","sign_citation":"https://pith.science/pith/2I7XDYKKU5LBB67ZJILI3INE5H/action/citation_signature","submit_replication":"https://pith.science/pith/2I7XDYKKU5LBB67ZJILI3INE5H/action/replication_record"}},"created_at":"2026-07-05T07:36:11.214428+00:00","updated_at":"2026-07-05T07:36:11.214428+00:00"}