{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:TUK4CV5IKD4ZHDWR737LP2L4A4","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":"eda52354ff4bdadff2175ff9a47fb1c857d45443e6e197af0faf4fc8b5804d78","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-04-24T02:53:22Z","title_canon_sha256":"4a980ac38aa59fbed7efbd85d4e72166ed8705637cd9c67879d8fdaa6ed4ab33"},"schema_version":"1.0","source":{"id":"2504.17210","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.17210","created_at":"2026-07-05T10:53:24Z"},{"alias_kind":"arxiv_version","alias_value":"2504.17210v1","created_at":"2026-07-05T10:53:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.17210","created_at":"2026-07-05T10:53:24Z"},{"alias_kind":"pith_short_12","alias_value":"TUK4CV5IKD4Z","created_at":"2026-07-05T10:53:24Z"},{"alias_kind":"pith_short_16","alias_value":"TUK4CV5IKD4ZHDWR","created_at":"2026-07-05T10:53:24Z"},{"alias_kind":"pith_short_8","alias_value":"TUK4CV5I","created_at":"2026-07-05T10:53:24Z"}],"graph_snapshots":[{"event_id":"sha256:23ae0c8807a9eb09b1078b18e1cf44d885d6dad0748cd4e7baa77280fcd2c88f","target":"graph","created_at":"2026-07-05T10:53:24Z","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/2504.17210/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Many data-driven modules in smart grid rely on access to high-quality power flow data; however, real-world data are often limited due to privacy and operational constraints. This paper presents a physics-informed generative framework based on Denoising Diffusion Probabilistic Models (DDPMs) for synthesizing feasible power flow data. By incorporating auxiliary training and physics-informed loss functions, the proposed method ensures that the generated data exhibit both statistical fidelity and adherence to power system feasibility. We evaluate the approach on the IEEE 14-bus and 30-bus benchmar","authors_text":"Darshana Upadhyay, Junfei Wang, Marzia Zaman, Pirathayini Srikantha","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-04-24T02:53:22Z","title":"Synthetic Power Flow Data Generation Using Physics-Informed Denoising Diffusion Probabilistic Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.17210","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:ad9c500ae0df3239a024d550b845a656658fd734f68c746ac6c39386c4ced780","target":"record","created_at":"2026-07-05T10:53:24Z","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":"eda52354ff4bdadff2175ff9a47fb1c857d45443e6e197af0faf4fc8b5804d78","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-04-24T02:53:22Z","title_canon_sha256":"4a980ac38aa59fbed7efbd85d4e72166ed8705637cd9c67879d8fdaa6ed4ab33"},"schema_version":"1.0","source":{"id":"2504.17210","kind":"arxiv","version":1}},"canonical_sha256":"9d15c157a850f9938ed1fefeb7e97c072e68a5a68dd725d1c24fd819de3ece25","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9d15c157a850f9938ed1fefeb7e97c072e68a5a68dd725d1c24fd819de3ece25","first_computed_at":"2026-07-05T10:53:24.295473Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:53:24.295473Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"+fzTyGzvVilav/s3ko5ZDptMcb3FhxGBIQRljQZkhC7VXny4FKNd2LE9sVoDEuCbsJLcVJ2NQEWXGlJpcOy2Cw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:53:24.295964Z","signed_message":"canonical_sha256_bytes"},"source_id":"2504.17210","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ad9c500ae0df3239a024d550b845a656658fd734f68c746ac6c39386c4ced780","sha256:23ae0c8807a9eb09b1078b18e1cf44d885d6dad0748cd4e7baa77280fcd2c88f"],"state_sha256":"609e81eb3c80e4a93007eff79719b6d900fe67a510ecaff8df3dfbebac2d3cb9"}