{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:GWBEU3BAN5AIEP4CDZA3I6X6L7","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":"22d0f3a405490be0295f941ae17491e527682c4d7d133c4ae85509a0fc36bdff","cross_cats_sorted":["cs.LG","cs.SY","eess.SP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SY","submitted_at":"2022-10-17T17:30:09Z","title_canon_sha256":"998f9682a1e8f738fcb17c07b4cfb9de4d998c8628fc3653f98bac7ba323febb"},"schema_version":"1.0","source":{"id":"2210.09277","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.09277","created_at":"2026-07-05T05:07:20Z"},{"alias_kind":"arxiv_version","alias_value":"2210.09277v1","created_at":"2026-07-05T05:07:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.09277","created_at":"2026-07-05T05:07:20Z"},{"alias_kind":"pith_short_12","alias_value":"GWBEU3BAN5AI","created_at":"2026-07-05T05:07:20Z"},{"alias_kind":"pith_short_16","alias_value":"GWBEU3BAN5AIEP4C","created_at":"2026-07-05T05:07:20Z"},{"alias_kind":"pith_short_8","alias_value":"GWBEU3BA","created_at":"2026-07-05T05:07:20Z"}],"graph_snapshots":[{"event_id":"sha256:91e799b3937553b314bcf3c1d8ff56608fd3f92f25b99c557f2aa1ea97899322","target":"graph","created_at":"2026-07-05T05:07:20Z","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/2210.09277/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Optimal power flow (OPF) is a critical optimization problem that allocates power to the generators in order to satisfy the demand at a minimum cost. Solving this problem exactly is computationally infeasible in the general case. In this work, we propose to leverage graph signal processing and machine learning. More specifically, we use a graph neural network to learn a nonlinear parametrization between the power demanded and the corresponding allocation. We learn the solution in an unsupervised manner, minimizing the cost directly. In order to take into account the electrical constraints of th","authors_text":"Alejandro Ribeiro, Damian Owerko, Fernando Gama","cross_cats":["cs.LG","cs.SY","eess.SP"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SY","submitted_at":"2022-10-17T17:30:09Z","title":"Unsupervised Optimal Power Flow Using Graph Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.09277","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":"sha256:5370426923a2b4a2b5e5cc9efb27279b69d7920f65061838aee41be284ad54d1","target":"record","created_at":"2026-07-05T05:07:20Z","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":"22d0f3a405490be0295f941ae17491e527682c4d7d133c4ae85509a0fc36bdff","cross_cats_sorted":["cs.LG","cs.SY","eess.SP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SY","submitted_at":"2022-10-17T17:30:09Z","title_canon_sha256":"998f9682a1e8f738fcb17c07b4cfb9de4d998c8628fc3653f98bac7ba323febb"},"schema_version":"1.0","source":{"id":"2210.09277","kind":"arxiv","version":1}},"canonical_sha256":"35824a6c206f40823f821e41b47afe5fcd741ca74ed69f812c35f94e47c31a99","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"35824a6c206f40823f821e41b47afe5fcd741ca74ed69f812c35f94e47c31a99","first_computed_at":"2026-07-05T05:07:20.829983Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:07:20.829983Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"vqJo2UYjlIXYf7j4mrLvamD1qrZMTPwyz0YhaT+C+XtE7fI+HCd/bCv+3zYeL+umk1PQsVDCbOoc75rmdG1FAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T05:07:20.830429Z","signed_message":"canonical_sha256_bytes"},"source_id":"2210.09277","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5370426923a2b4a2b5e5cc9efb27279b69d7920f65061838aee41be284ad54d1","sha256:91e799b3937553b314bcf3c1d8ff56608fd3f92f25b99c557f2aa1ea97899322"],"state_sha256":"971ba62d12076db11b76e3be886f02e9b463b15c98e0b465a15b02aa6ea1602b"}