{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:QDETDB6TTENKHO6RV7IXFYJDY2","short_pith_number":"pith:QDETDB6T","schema_version":"1.0","canonical_sha256":"80c93187d3991aa3bbd1afd172e123c6944aff916fb0f1a733ea4a56b3714670","source":{"kind":"arxiv","id":"2101.06768","version":1},"attestation_state":"computed","paper":{"title":"Spatial Network Decomposition for Fast and Scalable AC-OPF Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.SY","eess.SY"],"primary_cat":"cs.LG","authors_text":"Minas Chatzos, Pascal Van Hentenryck, Terrence W.K. Mak","submitted_at":"2021-01-17T20:09:11Z","abstract_excerpt":"This paper proposes a novel machine-learning approach for predicting AC-OPF solutions that features a fast and scalable training. It is motivated by the two critical considerations: (1) the fact that topology optimization and the stochasticity induced by renewable energy sources may lead to fundamentally different AC-OPF instances; and (2) the significant training time needed by existing machine-learning approaches for predicting AC-OPF. The proposed approach is a 2-stage methodology that exploits a spatial decomposition of the power network that is viewed as a set of regions. The first stage "},"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":"2101.06768","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-01-17T20:09:11Z","cross_cats_sorted":["cs.AI","cs.SY","eess.SY"],"title_canon_sha256":"9e7fa5fc9feba638992276b5a9f7151887f0fa9b24e65fbdea8f3c542a85edcd","abstract_canon_sha256":"8427b7aaa3c69e888c18f6238a0d743d23af7c4561c4eb7133fbfb5be0f1d23c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:07:36.902919Z","signature_b64":"wzvOIJ22Yqo63qqiQeu2Q+odrXw/vrVsM3WBey/2IJh6S5lcLfsOB/AJ+eUyxzdUK1PlnYcCjKlHIgw1JRj9Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"80c93187d3991aa3bbd1afd172e123c6944aff916fb0f1a733ea4a56b3714670","last_reissued_at":"2026-07-05T02:07:36.902531Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:07:36.902531Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Spatial Network Decomposition for Fast and Scalable AC-OPF Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.SY","eess.SY"],"primary_cat":"cs.LG","authors_text":"Minas Chatzos, Pascal Van Hentenryck, Terrence W.K. Mak","submitted_at":"2021-01-17T20:09:11Z","abstract_excerpt":"This paper proposes a novel machine-learning approach for predicting AC-OPF solutions that features a fast and scalable training. It is motivated by the two critical considerations: (1) the fact that topology optimization and the stochasticity induced by renewable energy sources may lead to fundamentally different AC-OPF instances; and (2) the significant training time needed by existing machine-learning approaches for predicting AC-OPF. The proposed approach is a 2-stage methodology that exploits a spatial decomposition of the power network that is viewed as a set of regions. The first stage "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2101.06768","kind":"arxiv","version":1},"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/2101.06768/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":"2101.06768","created_at":"2026-07-05T02:07:36.902593+00:00"},{"alias_kind":"arxiv_version","alias_value":"2101.06768v1","created_at":"2026-07-05T02:07:36.902593+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2101.06768","created_at":"2026-07-05T02:07:36.902593+00:00"},{"alias_kind":"pith_short_12","alias_value":"QDETDB6TTENK","created_at":"2026-07-05T02:07:36.902593+00:00"},{"alias_kind":"pith_short_16","alias_value":"QDETDB6TTENKHO6R","created_at":"2026-07-05T02:07:36.902593+00:00"},{"alias_kind":"pith_short_8","alias_value":"QDETDB6T","created_at":"2026-07-05T02:07:36.902593+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QDETDB6TTENKHO6RV7IXFYJDY2","json":"https://pith.science/pith/QDETDB6TTENKHO6RV7IXFYJDY2.json","graph_json":"https://pith.science/api/pith-number/QDETDB6TTENKHO6RV7IXFYJDY2/graph.json","events_json":"https://pith.science/api/pith-number/QDETDB6TTENKHO6RV7IXFYJDY2/events.json","paper":"https://pith.science/paper/QDETDB6T"},"agent_actions":{"view_html":"https://pith.science/pith/QDETDB6TTENKHO6RV7IXFYJDY2","download_json":"https://pith.science/pith/QDETDB6TTENKHO6RV7IXFYJDY2.json","view_paper":"https://pith.science/paper/QDETDB6T","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2101.06768&json=true","fetch_graph":"https://pith.science/api/pith-number/QDETDB6TTENKHO6RV7IXFYJDY2/graph.json","fetch_events":"https://pith.science/api/pith-number/QDETDB6TTENKHO6RV7IXFYJDY2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QDETDB6TTENKHO6RV7IXFYJDY2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QDETDB6TTENKHO6RV7IXFYJDY2/action/storage_attestation","attest_author":"https://pith.science/pith/QDETDB6TTENKHO6RV7IXFYJDY2/action/author_attestation","sign_citation":"https://pith.science/pith/QDETDB6TTENKHO6RV7IXFYJDY2/action/citation_signature","submit_replication":"https://pith.science/pith/QDETDB6TTENKHO6RV7IXFYJDY2/action/replication_record"}},"created_at":"2026-07-05T02:07:36.902593+00:00","updated_at":"2026-07-05T02:07:36.902593+00:00"}