{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:HQOIPASICOTXRSXAUYYPDUZ4TT","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"07cf8b629295fab0aeaf30931883c2b29bbaf463737eb3d2d2bf9c170bbc752e","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2023-06-27T06:53:37Z","title_canon_sha256":"1a0201fe674026d1fab4555cfc3362360079447619a29c27262917e367ba5c67"},"schema_version":"1.0","source":{"id":"2306.15243","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2306.15243","created_at":"2026-07-05T06:24:56Z"},{"alias_kind":"arxiv_version","alias_value":"2306.15243v1","created_at":"2026-07-05T06:24:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.15243","created_at":"2026-07-05T06:24:56Z"},{"alias_kind":"pith_short_12","alias_value":"HQOIPASICOTX","created_at":"2026-07-05T06:24:56Z"},{"alias_kind":"pith_short_16","alias_value":"HQOIPASICOTXRSXA","created_at":"2026-07-05T06:24:56Z"},{"alias_kind":"pith_short_8","alias_value":"HQOIPASI","created_at":"2026-07-05T06:24:56Z"}],"graph_snapshots":[{"event_id":"sha256:75a51cc693128324c5c3ad5b4629a7ec2563b095ce3700a72c3d89ed1e5e9509","target":"graph","created_at":"2026-07-05T06:24:56Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2306.15243/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Algorithmic differentiation (AD) has become increasingly capable and straightforward to use. However, AD is inefficient when applied directly to solvers, a feature of most engineering analyses. We can leverage implicit differentiation to define a general AD rule, making adjoints automatic. Furthermore, we can leverage the structure of differential equations to automate unsteady adjoints in a memory efficient way. We also derive a technique to speed up explicit differential equation solvers, which have no iterative solver to exploit. All of these techniques are demonstrated on problems of vario","authors_text":"Andrew Ning, Taylor McDonnell","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2023-06-27T06:53:37Z","title":"Automating Steady and Unsteady Adjoints: Efficiently Utilizing Implicit and Algorithmic Differentiation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.15243","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:3178dbb6fe4989d54113d6082f57c7004a540419f175dac066c66d94925877d2","target":"record","created_at":"2026-07-05T06:24:56Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"07cf8b629295fab0aeaf30931883c2b29bbaf463737eb3d2d2bf9c170bbc752e","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2023-06-27T06:53:37Z","title_canon_sha256":"1a0201fe674026d1fab4555cfc3362360079447619a29c27262917e367ba5c67"},"schema_version":"1.0","source":{"id":"2306.15243","kind":"arxiv","version":1}},"canonical_sha256":"3c1c87824813a778cae0a630f1d33c9ce751f913dfe3286c62be5855d62bc356","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3c1c87824813a778cae0a630f1d33c9ce751f913dfe3286c62be5855d62bc356","first_computed_at":"2026-07-05T06:24:56.503360Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:24:56.503360Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"fb3g4o/yTo5ki/znuqnkvAeoMshB3mrgaNlEAiciYBOXsCc2xttDq8x9YZ3IfamdEOey+QrPM8VeLeMBZEaGDw==","signature_status":"signed_v1","signed_at":"2026-07-05T06:24:56.503766Z","signed_message":"canonical_sha256_bytes"},"source_id":"2306.15243","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3178dbb6fe4989d54113d6082f57c7004a540419f175dac066c66d94925877d2","sha256:75a51cc693128324c5c3ad5b4629a7ec2563b095ce3700a72c3d89ed1e5e9509"],"state_sha256":"b905a2b28788d01d5b179660b016432eb5dfbe0afdd4f993bdb2e6005bdcb7a4"}