{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:YSBPAEOBISDEZIXOWL6PDGXKPK","short_pith_number":"pith:YSBPAEOB","schema_version":"1.0","canonical_sha256":"c482f011c144864ca2eeb2fcf19aea7aac9e46e469bdee87aba213c7682e215c","source":{"kind":"arxiv","id":"1812.01892","version":2},"attestation_state":"computed","paper":{"title":"A Comparison of Automatic Differentiation and Continuous Sensitivity Analysis for Derivatives of Differential Equation Solutions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NA"],"primary_cat":"math.NA","authors_text":"Christopher Rackauckas, Mike Innes, Vaibhav Dixit, Xingjian Guo, Yingbo Ma","submitted_at":"2018-12-05T10:15:32Z","abstract_excerpt":"Derivatives of differential equation solutions are commonly for parameter estimation, fitting neural differential equations, and as model diagnostics. However, with a litany of choices and a Cartesian product of potential methods, it can be difficult for practitioners to understand which method is likely to be the most effective on their particular application. In this manuscript we investigate the performance characteristics of Discrete Local Sensitivity Analysis implemented via Automatic Differentiation (DSAAD) against continuous adjoint sensitivity analysis. Non-stiff and stiff biological a"},"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":"1812.01892","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2018-12-05T10:15:32Z","cross_cats_sorted":["cs.NA"],"title_canon_sha256":"519b0184f8de1ed03f3a441b394758c466cd10752f967c1c8c09af51056dd2c7","abstract_canon_sha256":"513a406c7e237feba44bc0c0ad96e3e5d091a24dfc5bbd592ca2e272c8269a76"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-04T20:13:45.637689Z","signature_b64":"MoQ4cnXZDKBWDty60AdiAefWadKnq4FOxU5VMsqh9+CVLQNFHS5IXC/287ysGOH/oiLzgdLirvHey2tO509rAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c482f011c144864ca2eeb2fcf19aea7aac9e46e469bdee87aba213c7682e215c","last_reissued_at":"2026-06-04T20:13:45.636865Z","signature_status":"signed_v1","first_computed_at":"2026-06-04T20:13:45.636865Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Comparison of Automatic Differentiation and Continuous Sensitivity Analysis for Derivatives of Differential Equation Solutions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NA"],"primary_cat":"math.NA","authors_text":"Christopher Rackauckas, Mike Innes, Vaibhav Dixit, Xingjian Guo, Yingbo Ma","submitted_at":"2018-12-05T10:15:32Z","abstract_excerpt":"Derivatives of differential equation solutions are commonly for parameter estimation, fitting neural differential equations, and as model diagnostics. However, with a litany of choices and a Cartesian product of potential methods, it can be difficult for practitioners to understand which method is likely to be the most effective on their particular application. In this manuscript we investigate the performance characteristics of Discrete Local Sensitivity Analysis implemented via Automatic Differentiation (DSAAD) against continuous adjoint sensitivity analysis. Non-stiff and stiff biological a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.01892","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/1812.01892/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":"1812.01892","created_at":"2026-06-04T20:13:45.636963+00:00"},{"alias_kind":"arxiv_version","alias_value":"1812.01892v2","created_at":"2026-06-04T20:13:45.636963+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.01892","created_at":"2026-06-04T20:13:45.636963+00:00"},{"alias_kind":"pith_short_12","alias_value":"YSBPAEOBISDE","created_at":"2026-06-04T20:13:45.636963+00:00"},{"alias_kind":"pith_short_16","alias_value":"YSBPAEOBISDEZIXO","created_at":"2026-06-04T20:13:45.636963+00:00"},{"alias_kind":"pith_short_8","alias_value":"YSBPAEOB","created_at":"2026-06-04T20:13:45.636963+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":3,"sample":[{"citing_arxiv_id":"1906.11365","citing_title":"Parameter Estimation and Uncertainty Quantification for Systems Biology Models","ref_index":33,"is_internal_anchor":true},{"citing_arxiv_id":"1907.07587","citing_title":"A Differentiable Programming System to Bridge Machine Learning and Scientific Computing","ref_index":43,"is_internal_anchor":true},{"citing_arxiv_id":"2001.04385","citing_title":"Universal Differential Equations for Scientific Machine Learning","ref_index":44,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YSBPAEOBISDEZIXOWL6PDGXKPK","json":"https://pith.science/pith/YSBPAEOBISDEZIXOWL6PDGXKPK.json","graph_json":"https://pith.science/api/pith-number/YSBPAEOBISDEZIXOWL6PDGXKPK/graph.json","events_json":"https://pith.science/api/pith-number/YSBPAEOBISDEZIXOWL6PDGXKPK/events.json","paper":"https://pith.science/paper/YSBPAEOB"},"agent_actions":{"view_html":"https://pith.science/pith/YSBPAEOBISDEZIXOWL6PDGXKPK","download_json":"https://pith.science/pith/YSBPAEOBISDEZIXOWL6PDGXKPK.json","view_paper":"https://pith.science/paper/YSBPAEOB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1812.01892&json=true","fetch_graph":"https://pith.science/api/pith-number/YSBPAEOBISDEZIXOWL6PDGXKPK/graph.json","fetch_events":"https://pith.science/api/pith-number/YSBPAEOBISDEZIXOWL6PDGXKPK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YSBPAEOBISDEZIXOWL6PDGXKPK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YSBPAEOBISDEZIXOWL6PDGXKPK/action/storage_attestation","attest_author":"https://pith.science/pith/YSBPAEOBISDEZIXOWL6PDGXKPK/action/author_attestation","sign_citation":"https://pith.science/pith/YSBPAEOBISDEZIXOWL6PDGXKPK/action/citation_signature","submit_replication":"https://pith.science/pith/YSBPAEOBISDEZIXOWL6PDGXKPK/action/replication_record"}},"created_at":"2026-06-04T20:13:45.636963+00:00","updated_at":"2026-06-04T20:13:45.636963+00:00"}