{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:7OV4QIM5Y7RPXOR52GBS5TQHJB","short_pith_number":"pith:7OV4QIM5","schema_version":"1.0","canonical_sha256":"fbabc8219dc7e2fbba3dd1832ece07485e914fea561c426f1be0ff1cb6040223","source":{"kind":"arxiv","id":"1902.02376","version":1},"attestation_state":"computed","paper":{"title":"DiffEqFlux.jl - A Julia Library for Neural Differential Equations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Chris Rackauckas, Jesse Bettencourt, Lyndon White, Mike Innes, Vaibhav Dixit, Yingbo Ma","submitted_at":"2019-02-06T19:42:14Z","abstract_excerpt":"DiffEqFlux.jl is a library for fusing neural networks and differential equations. In this work we describe differential equations from the viewpoint of data science and discuss the complementary nature between machine learning models and differential equations. We demonstrate the ability to incorporate DifferentialEquations.jl-defined differential equation problems into a Flux-defined neural network, and vice versa. The advantages of being able to use the entire DifferentialEquations.jl suite for this purpose is demonstrated by counter examples where simple integration strategies fail, but the"},"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":"1902.02376","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-02-06T19:42:14Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"e9e725fce32620f57a10c0bc07f6bd3d34b4d373e192f2e4ff363634f75d609f","abstract_canon_sha256":"c2ea12b071f584bac600ad7cc30999e9dd902581146145667e74c9d1b1ddee7b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:54:33.612138Z","signature_b64":"EM2hTBcH6iCNRZBudQnFlM8DrinwaqJZUlPaJBHGqcQU3QjYxIamrQS008ALcAiO2abv/KnbbDDN4NEpluhVCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fbabc8219dc7e2fbba3dd1832ece07485e914fea561c426f1be0ff1cb6040223","last_reissued_at":"2026-05-17T23:54:33.611346Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:54:33.611346Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DiffEqFlux.jl - A Julia Library for Neural Differential Equations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Chris Rackauckas, Jesse Bettencourt, Lyndon White, Mike Innes, Vaibhav Dixit, Yingbo Ma","submitted_at":"2019-02-06T19:42:14Z","abstract_excerpt":"DiffEqFlux.jl is a library for fusing neural networks and differential equations. In this work we describe differential equations from the viewpoint of data science and discuss the complementary nature between machine learning models and differential equations. We demonstrate the ability to incorporate DifferentialEquations.jl-defined differential equation problems into a Flux-defined neural network, and vice versa. The advantages of being able to use the entire DifferentialEquations.jl suite for this purpose is demonstrated by counter examples where simple integration strategies fail, but the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.02376","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":""},"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":"1902.02376","created_at":"2026-05-17T23:54:33.611477+00:00"},{"alias_kind":"arxiv_version","alias_value":"1902.02376v1","created_at":"2026-05-17T23:54:33.611477+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.02376","created_at":"2026-05-17T23:54:33.611477+00:00"},{"alias_kind":"pith_short_12","alias_value":"7OV4QIM5Y7RP","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_16","alias_value":"7OV4QIM5Y7RPXOR5","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_8","alias_value":"7OV4QIM5","created_at":"2026-05-18T12:33:12.712433+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":3,"sample":[{"citing_arxiv_id":"2605.23510","citing_title":"Learning partially observed systems with neural Hamiltonian ordinary differential equations","ref_index":15,"is_internal_anchor":true},{"citing_arxiv_id":"1907.07587","citing_title":"A Differentiable Programming System to Bridge Machine Learning and Scientific Computing","ref_index":42,"is_internal_anchor":true},{"citing_arxiv_id":"2509.00203","citing_title":"Estimating Parameter Fields in Multi-Physics PDEs from Scarce Measurements","ref_index":63,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7OV4QIM5Y7RPXOR52GBS5TQHJB","json":"https://pith.science/pith/7OV4QIM5Y7RPXOR52GBS5TQHJB.json","graph_json":"https://pith.science/api/pith-number/7OV4QIM5Y7RPXOR52GBS5TQHJB/graph.json","events_json":"https://pith.science/api/pith-number/7OV4QIM5Y7RPXOR52GBS5TQHJB/events.json","paper":"https://pith.science/paper/7OV4QIM5"},"agent_actions":{"view_html":"https://pith.science/pith/7OV4QIM5Y7RPXOR52GBS5TQHJB","download_json":"https://pith.science/pith/7OV4QIM5Y7RPXOR52GBS5TQHJB.json","view_paper":"https://pith.science/paper/7OV4QIM5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1902.02376&json=true","fetch_graph":"https://pith.science/api/pith-number/7OV4QIM5Y7RPXOR52GBS5TQHJB/graph.json","fetch_events":"https://pith.science/api/pith-number/7OV4QIM5Y7RPXOR52GBS5TQHJB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7OV4QIM5Y7RPXOR52GBS5TQHJB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7OV4QIM5Y7RPXOR52GBS5TQHJB/action/storage_attestation","attest_author":"https://pith.science/pith/7OV4QIM5Y7RPXOR52GBS5TQHJB/action/author_attestation","sign_citation":"https://pith.science/pith/7OV4QIM5Y7RPXOR52GBS5TQHJB/action/citation_signature","submit_replication":"https://pith.science/pith/7OV4QIM5Y7RPXOR52GBS5TQHJB/action/replication_record"}},"created_at":"2026-05-17T23:54:33.611477+00:00","updated_at":"2026-05-17T23:54:33.611477+00:00"}