{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:UAFF5AFTDAE53VTGOYMMAQLVNC","short_pith_number":"pith:UAFF5AFT","schema_version":"1.0","canonical_sha256":"a00a5e80b31809ddd6667618c041756891e3f9ff21758fa40e24e17c9a3863cf","source":{"kind":"arxiv","id":"1902.05607","version":1},"attestation_state":"computed","paper":{"title":"Learning for DC-OPF: Classifying active sets using neural nets","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SY","authors_text":"Deepjyoti Deka, Sidhant Misra","submitted_at":"2019-02-14T21:12:24Z","abstract_excerpt":"The optimal power flow is an optimization problem used in power systems operational planning to maximize economic efficiency while satisfying demand and maintaining safety margins. Due to uncertainty and variability in renewable energy generation and demand, the optimal solution needs to be updated in response to observed uncertainty realizations or near real-time forecast updates. To address the challenge of computing such frequent real-time updates to the optimal solution, recent literature has proposed the use of machine learning to learn the mapping between the uncertainty realization and "},"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":"1902.05607","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SY","submitted_at":"2019-02-14T21:12:24Z","cross_cats_sorted":[],"title_canon_sha256":"2b2ef5a095909563935cacbffa1d2e4c6e71c9090312fadcf30e1004d4c34c0b","abstract_canon_sha256":"244aedfb7c151bb7095bfcadde4de98ca06e84d7ee2234d86ba737359a851a24"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:53:56.803547Z","signature_b64":"i8WvPGBAp6/jMxiJB11PbUjWS4jWlm6V0NKjbWEo143l3ZbsAlYhNmDzIxrjim3YTtGB2ql3b7OxXmeaoGuTBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a00a5e80b31809ddd6667618c041756891e3f9ff21758fa40e24e17c9a3863cf","last_reissued_at":"2026-05-17T23:53:56.802847Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:53:56.802847Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning for DC-OPF: Classifying active sets using neural nets","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SY","authors_text":"Deepjyoti Deka, Sidhant Misra","submitted_at":"2019-02-14T21:12:24Z","abstract_excerpt":"The optimal power flow is an optimization problem used in power systems operational planning to maximize economic efficiency while satisfying demand and maintaining safety margins. Due to uncertainty and variability in renewable energy generation and demand, the optimal solution needs to be updated in response to observed uncertainty realizations or near real-time forecast updates. To address the challenge of computing such frequent real-time updates to the optimal solution, recent literature has proposed the use of machine learning to learn the mapping between the uncertainty realization and "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.05607","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":""},"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":"1902.05607","created_at":"2026-05-17T23:53:56.802963+00:00"},{"alias_kind":"arxiv_version","alias_value":"1902.05607v1","created_at":"2026-05-17T23:53:56.802963+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.05607","created_at":"2026-05-17T23:53:56.802963+00:00"},{"alias_kind":"pith_short_12","alias_value":"UAFF5AFTDAE5","created_at":"2026-05-18T12:33:30.264802+00:00"},{"alias_kind":"pith_short_16","alias_value":"UAFF5AFTDAE53VTG","created_at":"2026-05-18T12:33:30.264802+00:00"},{"alias_kind":"pith_short_8","alias_value":"UAFF5AFT","created_at":"2026-05-18T12:33:30.264802+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/UAFF5AFTDAE53VTGOYMMAQLVNC","json":"https://pith.science/pith/UAFF5AFTDAE53VTGOYMMAQLVNC.json","graph_json":"https://pith.science/api/pith-number/UAFF5AFTDAE53VTGOYMMAQLVNC/graph.json","events_json":"https://pith.science/api/pith-number/UAFF5AFTDAE53VTGOYMMAQLVNC/events.json","paper":"https://pith.science/paper/UAFF5AFT"},"agent_actions":{"view_html":"https://pith.science/pith/UAFF5AFTDAE53VTGOYMMAQLVNC","download_json":"https://pith.science/pith/UAFF5AFTDAE53VTGOYMMAQLVNC.json","view_paper":"https://pith.science/paper/UAFF5AFT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1902.05607&json=true","fetch_graph":"https://pith.science/api/pith-number/UAFF5AFTDAE53VTGOYMMAQLVNC/graph.json","fetch_events":"https://pith.science/api/pith-number/UAFF5AFTDAE53VTGOYMMAQLVNC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UAFF5AFTDAE53VTGOYMMAQLVNC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UAFF5AFTDAE53VTGOYMMAQLVNC/action/storage_attestation","attest_author":"https://pith.science/pith/UAFF5AFTDAE53VTGOYMMAQLVNC/action/author_attestation","sign_citation":"https://pith.science/pith/UAFF5AFTDAE53VTGOYMMAQLVNC/action/citation_signature","submit_replication":"https://pith.science/pith/UAFF5AFTDAE53VTGOYMMAQLVNC/action/replication_record"}},"created_at":"2026-05-17T23:53:56.802963+00:00","updated_at":"2026-05-17T23:53:56.802963+00:00"}