{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:GUGODSUOUZRPQYF2TVEJ4CM2SM","short_pith_number":"pith:GUGODSUO","schema_version":"1.0","canonical_sha256":"350ce1ca8ea662f860ba9d489e099a931618cb81593ce6c2f73a3b7b5805903e","source":{"kind":"arxiv","id":"1910.05117","version":2},"attestation_state":"computed","paper":{"title":"Data-driven discovery of free-form governing differential equations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","physics.comp-ph","stat.ML"],"primary_cat":"cs.CE","authors_text":"Genghis Khan, Liping Wang, Philippe Hawi, Roger Ghanem, Steven Atkinson, Waad Subber","submitted_at":"2019-09-27T02:12:19Z","abstract_excerpt":"We present a method of discovering governing differential equations from data without the need to specify a priori the terms to appear in the equation. The input to our method is a dataset (or ensemble of datasets) corresponding to a particular solution (or ensemble of particular solutions) of a differential equation. The output is a human-readable differential equation with parameters calibrated to the individual particular solutions provided. The key to our method is to learn differentiable models of the data that subsequently serve as inputs to a genetic programming algorithm in which graph"},"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":"1910.05117","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CE","submitted_at":"2019-09-27T02:12:19Z","cross_cats_sorted":["cs.LG","physics.comp-ph","stat.ML"],"title_canon_sha256":"df9a89e80408c0ed38e79b2446b968e4bbd88359cbadd7a1d7f15f715a7fbe8a","abstract_canon_sha256":"b01f17243be5d04c1d9ca53e801acb19290c1793bfd6c061f591656a8d3e7681"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:18:26.902677Z","signature_b64":"lYq+CGmGncHaXIltaH3OVLoG8p151J9LkCwOCvuWHUXpamgPLJJYd3097Ox/tgjZ9xpKzWaaLSTXrzkHDJ2eDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"350ce1ca8ea662f860ba9d489e099a931618cb81593ce6c2f73a3b7b5805903e","last_reissued_at":"2026-07-05T00:18:26.902203Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:18:26.902203Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Data-driven discovery of free-form governing differential equations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","physics.comp-ph","stat.ML"],"primary_cat":"cs.CE","authors_text":"Genghis Khan, Liping Wang, Philippe Hawi, Roger Ghanem, Steven Atkinson, Waad Subber","submitted_at":"2019-09-27T02:12:19Z","abstract_excerpt":"We present a method of discovering governing differential equations from data without the need to specify a priori the terms to appear in the equation. The input to our method is a dataset (or ensemble of datasets) corresponding to a particular solution (or ensemble of particular solutions) of a differential equation. The output is a human-readable differential equation with parameters calibrated to the individual particular solutions provided. The key to our method is to learn differentiable models of the data that subsequently serve as inputs to a genetic programming algorithm in which graph"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.05117","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/1910.05117/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":"1910.05117","created_at":"2026-07-05T00:18:26.902257+00:00"},{"alias_kind":"arxiv_version","alias_value":"1910.05117v2","created_at":"2026-07-05T00:18:26.902257+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.05117","created_at":"2026-07-05T00:18:26.902257+00:00"},{"alias_kind":"pith_short_12","alias_value":"GUGODSUOUZRP","created_at":"2026-07-05T00:18:26.902257+00:00"},{"alias_kind":"pith_short_16","alias_value":"GUGODSUOUZRPQYF2","created_at":"2026-07-05T00:18:26.902257+00:00"},{"alias_kind":"pith_short_8","alias_value":"GUGODSUO","created_at":"2026-07-05T00:18:26.902257+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.19145","citing_title":"OrthoReg: Orthogonal Regularization for Hybrid Symbolic-Neural Dynamical Systems","ref_index":31,"is_internal_anchor":false},{"citing_arxiv_id":"2305.01582","citing_title":"Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl","ref_index":35,"is_internal_anchor":false},{"citing_arxiv_id":"2604.18402","citing_title":"Adaptive Kernel Selection for Kernelized Diffusion Maps","ref_index":65,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GUGODSUOUZRPQYF2TVEJ4CM2SM","json":"https://pith.science/pith/GUGODSUOUZRPQYF2TVEJ4CM2SM.json","graph_json":"https://pith.science/api/pith-number/GUGODSUOUZRPQYF2TVEJ4CM2SM/graph.json","events_json":"https://pith.science/api/pith-number/GUGODSUOUZRPQYF2TVEJ4CM2SM/events.json","paper":"https://pith.science/paper/GUGODSUO"},"agent_actions":{"view_html":"https://pith.science/pith/GUGODSUOUZRPQYF2TVEJ4CM2SM","download_json":"https://pith.science/pith/GUGODSUOUZRPQYF2TVEJ4CM2SM.json","view_paper":"https://pith.science/paper/GUGODSUO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1910.05117&json=true","fetch_graph":"https://pith.science/api/pith-number/GUGODSUOUZRPQYF2TVEJ4CM2SM/graph.json","fetch_events":"https://pith.science/api/pith-number/GUGODSUOUZRPQYF2TVEJ4CM2SM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GUGODSUOUZRPQYF2TVEJ4CM2SM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GUGODSUOUZRPQYF2TVEJ4CM2SM/action/storage_attestation","attest_author":"https://pith.science/pith/GUGODSUOUZRPQYF2TVEJ4CM2SM/action/author_attestation","sign_citation":"https://pith.science/pith/GUGODSUOUZRPQYF2TVEJ4CM2SM/action/citation_signature","submit_replication":"https://pith.science/pith/GUGODSUOUZRPQYF2TVEJ4CM2SM/action/replication_record"}},"created_at":"2026-07-05T00:18:26.902257+00:00","updated_at":"2026-07-05T00:18:26.902257+00:00"}