{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:OTYO5TQI2E576QPEP2ECMHGC7L","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":"d4012b11676d353dfeb8b365b78cc15fdafef762e56bf1516bf761682562a618","cross_cats_sorted":["cs.NA"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2025-05-31T23:18:09Z","title_canon_sha256":"b5aaca8b7094d053c9e10f7ccde7b815b52d0a01650d9817839e8dcd7c4c24f8"},"schema_version":"1.0","source":{"id":"2506.00746","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.00746","created_at":"2026-07-05T11:13:39Z"},{"alias_kind":"arxiv_version","alias_value":"2506.00746v1","created_at":"2026-07-05T11:13:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.00746","created_at":"2026-07-05T11:13:39Z"},{"alias_kind":"pith_short_12","alias_value":"OTYO5TQI2E57","created_at":"2026-07-05T11:13:39Z"},{"alias_kind":"pith_short_16","alias_value":"OTYO5TQI2E576QPE","created_at":"2026-07-05T11:13:39Z"},{"alias_kind":"pith_short_8","alias_value":"OTYO5TQI","created_at":"2026-07-05T11:13:39Z"}],"graph_snapshots":[{"event_id":"sha256:d5458d7b184336e3b4cd51196b7714c1a984073b6f53b54f61592ca914881b89","target":"graph","created_at":"2026-07-05T11:13:39Z","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/2506.00746/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This article aims to demonstrate and discuss the applications of automatic differentiation (AD) for finding derivatives in PDE-constrained optimization problems and Jacobians in non-linear finite element analysis. The main idea is to localize the application of AD at the integration point level by combining it with the so-called Finite Element Operator Decomposition. The proposed methods are computationally effective, scalable, automatic, and non-intrusive, making them ideal for existing serial and parallel solvers and complex multiphysics applications. The performance is demonstrated on large","authors_text":"Boyan Lazarov, Julian Andrej, Tzanio Kolev","cross_cats":["cs.NA"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2025-05-31T23:18:09Z","title":"Scalable Analysis and Design Using Automatic Differentiation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.00746","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:ccff38090499f936a8b70c52bdd4e2da37afa47a00650f829fc44f26f98d4f4a","target":"record","created_at":"2026-07-05T11:13:39Z","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":"d4012b11676d353dfeb8b365b78cc15fdafef762e56bf1516bf761682562a618","cross_cats_sorted":["cs.NA"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2025-05-31T23:18:09Z","title_canon_sha256":"b5aaca8b7094d053c9e10f7ccde7b815b52d0a01650d9817839e8dcd7c4c24f8"},"schema_version":"1.0","source":{"id":"2506.00746","kind":"arxiv","version":1}},"canonical_sha256":"74f0eece08d13bff41e47e88261cc2fad4627b699df939a8783aa80066190d9e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"74f0eece08d13bff41e47e88261cc2fad4627b699df939a8783aa80066190d9e","first_computed_at":"2026-07-05T11:13:39.851351Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:13:39.851351Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/Pb5q0t7ZKBWM4Ew+TsYknhzsArAxkGd3NyhcYQuOAtA7zOhobKBLwOKfiQDkKNt4AsALpgHAZd+Ika0XEDMDw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:13:39.851773Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.00746","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ccff38090499f936a8b70c52bdd4e2da37afa47a00650f829fc44f26f98d4f4a","sha256:d5458d7b184336e3b4cd51196b7714c1a984073b6f53b54f61592ca914881b89"],"state_sha256":"666932c332d800fa2c7fe2294e9a02575538f626a759ec6a75f59ed7116fa87f"}