{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:6YHD5TBP3N4OR4PKZ3PMSCDZW6","short_pith_number":"pith:6YHD5TBP","schema_version":"1.0","canonical_sha256":"f60e3ecc2fdb78e8f1eacedec90879b79ece038e5d0b346da92434be36093c5b","source":{"kind":"arxiv","id":"2407.15727","version":2},"attestation_state":"computed","paper":{"title":"Inferring turbulent velocity and temperature fields and their statistics from Lagrangian velocity measurements using physics-informed Kolmogorov-Arnold Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","physics.comp-ph"],"primary_cat":"physics.flu-dyn","authors_text":"Christian Cierpka, George Em Karniadakis, Juan Diego Toscano, Martin Maxey, Theo K\\\"aufer, Zhibo Wang","submitted_at":"2024-07-22T15:30:21Z","abstract_excerpt":"We propose the Artificial Intelligence Velocimetry-Thermometry (AIVT) method to infer hidden temperature fields from experimental turbulent velocity data. This physics-informed machine learning method enables us to infer continuous temperature fields using only sparse velocity data, hence eliminating the need for direct temperature measurements. Specifically, AIVT is based on physics-informed Kolmogorov-Arnold Networks (not neural networks) and is trained by optimizing a combined loss function that minimizes the residuals of the velocity data, boundary conditions, and the governing equations. "},"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":"2407.15727","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.flu-dyn","submitted_at":"2024-07-22T15:30:21Z","cross_cats_sorted":["cs.LG","physics.comp-ph"],"title_canon_sha256":"b95755c4645ef01f598209cc64dc9cabb74ad4f3fe435683cb96202cb09a70a2","abstract_canon_sha256":"b72f20662791c86791258bd142e2f6b23a4c64c5fa57d6155d4a5f5d8cc8bed4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:47:23.223439Z","signature_b64":"ATFKO2nfGTlIfEzo7SvI4IQl2fGe56+cc5aSIUcmK2ApiqwbB9FLxNERoUri8u1SYgGAxZTOdMDzJ9PqIU5hAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f60e3ecc2fdb78e8f1eacedec90879b79ece038e5d0b346da92434be36093c5b","last_reissued_at":"2026-07-05T08:47:23.222933Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:47:23.222933Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Inferring turbulent velocity and temperature fields and their statistics from Lagrangian velocity measurements using physics-informed Kolmogorov-Arnold Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","physics.comp-ph"],"primary_cat":"physics.flu-dyn","authors_text":"Christian Cierpka, George Em Karniadakis, Juan Diego Toscano, Martin Maxey, Theo K\\\"aufer, Zhibo Wang","submitted_at":"2024-07-22T15:30:21Z","abstract_excerpt":"We propose the Artificial Intelligence Velocimetry-Thermometry (AIVT) method to infer hidden temperature fields from experimental turbulent velocity data. This physics-informed machine learning method enables us to infer continuous temperature fields using only sparse velocity data, hence eliminating the need for direct temperature measurements. Specifically, AIVT is based on physics-informed Kolmogorov-Arnold Networks (not neural networks) and is trained by optimizing a combined loss function that minimizes the residuals of the velocity data, boundary conditions, and the governing equations. "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.15727","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/2407.15727/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":"2407.15727","created_at":"2026-07-05T08:47:23.222997+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.15727v2","created_at":"2026-07-05T08:47:23.222997+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.15727","created_at":"2026-07-05T08:47:23.222997+00:00"},{"alias_kind":"pith_short_12","alias_value":"6YHD5TBP3N4O","created_at":"2026-07-05T08:47:23.222997+00:00"},{"alias_kind":"pith_short_16","alias_value":"6YHD5TBP3N4OR4PK","created_at":"2026-07-05T08:47:23.222997+00:00"},{"alias_kind":"pith_short_8","alias_value":"6YHD5TBP","created_at":"2026-07-05T08:47:23.222997+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.17582","citing_title":"Physics-informed, boundary-constrained Gaussian process regression for the reconstruction of fluid flow fields","ref_index":31,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6YHD5TBP3N4OR4PKZ3PMSCDZW6","json":"https://pith.science/pith/6YHD5TBP3N4OR4PKZ3PMSCDZW6.json","graph_json":"https://pith.science/api/pith-number/6YHD5TBP3N4OR4PKZ3PMSCDZW6/graph.json","events_json":"https://pith.science/api/pith-number/6YHD5TBP3N4OR4PKZ3PMSCDZW6/events.json","paper":"https://pith.science/paper/6YHD5TBP"},"agent_actions":{"view_html":"https://pith.science/pith/6YHD5TBP3N4OR4PKZ3PMSCDZW6","download_json":"https://pith.science/pith/6YHD5TBP3N4OR4PKZ3PMSCDZW6.json","view_paper":"https://pith.science/paper/6YHD5TBP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.15727&json=true","fetch_graph":"https://pith.science/api/pith-number/6YHD5TBP3N4OR4PKZ3PMSCDZW6/graph.json","fetch_events":"https://pith.science/api/pith-number/6YHD5TBP3N4OR4PKZ3PMSCDZW6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6YHD5TBP3N4OR4PKZ3PMSCDZW6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6YHD5TBP3N4OR4PKZ3PMSCDZW6/action/storage_attestation","attest_author":"https://pith.science/pith/6YHD5TBP3N4OR4PKZ3PMSCDZW6/action/author_attestation","sign_citation":"https://pith.science/pith/6YHD5TBP3N4OR4PKZ3PMSCDZW6/action/citation_signature","submit_replication":"https://pith.science/pith/6YHD5TBP3N4OR4PKZ3PMSCDZW6/action/replication_record"}},"created_at":"2026-07-05T08:47:23.222997+00:00","updated_at":"2026-07-05T08:47:23.222997+00:00"}