{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:QZBVXM7HRRLPKUNZH5OF5VJJEL","short_pith_number":"pith:QZBVXM7H","schema_version":"1.0","canonical_sha256":"86435bb3e78c56f551b93f5c5ed52922f022b934cc58e24fd17bb4fd2eb5bee2","source":{"kind":"arxiv","id":"1910.13613","version":2},"attestation_state":"computed","paper":{"title":"Bounding Regression Errors in Data-driven Power Grid Steady-state Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","eess.SP","math.IT","stat.ML"],"primary_cat":"cs.LG","authors_text":"Audun Botterud, Bolun Xu, Chongqing Kang, Ning Zhang, Yuxiao Liu","submitted_at":"2019-10-30T01:20:10Z","abstract_excerpt":"Data-driven models analyze power grids under incomplete physical information, and their accuracy has been mostly validated empirically using certain training and testing datasets. This paper explores error bounds for data-driven models under all possible training and testing scenarios, and proposes an evaluation implementation based on Rademacher complexity theory. We answer key questions for data-driven models: how much training data is required to guarantee a certain error bound, and how partial physical knowledge can be utilized to reduce the required amount of data. Our results are crucial"},"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":"1910.13613","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-10-30T01:20:10Z","cross_cats_sorted":["cs.IT","eess.SP","math.IT","stat.ML"],"title_canon_sha256":"b35ea9cc297ad51da47af4c64bd74e7d69dabfe618bd2377a0f8a3d21e6b077a","abstract_canon_sha256":"cd91e28b828c9011c03723d925fc75645914f35c2d5ec1b98499c3aa55e1b9f9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:05:38.197583Z","signature_b64":"n0zkmfZYy9owr9IIlJ/6PPLuuQ59e0s93F9FJlU27b2v6hf+03gLeilXY7JYR3J/xKYexRnhtIFTPV0P1EflDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"86435bb3e78c56f551b93f5c5ed52922f022b934cc58e24fd17bb4fd2eb5bee2","last_reissued_at":"2026-07-05T01:05:38.197157Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:05:38.197157Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Bounding Regression Errors in Data-driven Power Grid Steady-state Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","eess.SP","math.IT","stat.ML"],"primary_cat":"cs.LG","authors_text":"Audun Botterud, Bolun Xu, Chongqing Kang, Ning Zhang, Yuxiao Liu","submitted_at":"2019-10-30T01:20:10Z","abstract_excerpt":"Data-driven models analyze power grids under incomplete physical information, and their accuracy has been mostly validated empirically using certain training and testing datasets. This paper explores error bounds for data-driven models under all possible training and testing scenarios, and proposes an evaluation implementation based on Rademacher complexity theory. We answer key questions for data-driven models: how much training data is required to guarantee a certain error bound, and how partial physical knowledge can be utilized to reduce the required amount of data. Our results are crucial"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.13613","kind":"arxiv","version":2},"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/1910.13613/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":"1910.13613","created_at":"2026-07-05T01:05:38.197212+00:00"},{"alias_kind":"arxiv_version","alias_value":"1910.13613v2","created_at":"2026-07-05T01:05:38.197212+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.13613","created_at":"2026-07-05T01:05:38.197212+00:00"},{"alias_kind":"pith_short_12","alias_value":"QZBVXM7HRRLP","created_at":"2026-07-05T01:05:38.197212+00:00"},{"alias_kind":"pith_short_16","alias_value":"QZBVXM7HRRLPKUNZ","created_at":"2026-07-05T01:05:38.197212+00:00"},{"alias_kind":"pith_short_8","alias_value":"QZBVXM7H","created_at":"2026-07-05T01:05:38.197212+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/QZBVXM7HRRLPKUNZH5OF5VJJEL","json":"https://pith.science/pith/QZBVXM7HRRLPKUNZH5OF5VJJEL.json","graph_json":"https://pith.science/api/pith-number/QZBVXM7HRRLPKUNZH5OF5VJJEL/graph.json","events_json":"https://pith.science/api/pith-number/QZBVXM7HRRLPKUNZH5OF5VJJEL/events.json","paper":"https://pith.science/paper/QZBVXM7H"},"agent_actions":{"view_html":"https://pith.science/pith/QZBVXM7HRRLPKUNZH5OF5VJJEL","download_json":"https://pith.science/pith/QZBVXM7HRRLPKUNZH5OF5VJJEL.json","view_paper":"https://pith.science/paper/QZBVXM7H","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1910.13613&json=true","fetch_graph":"https://pith.science/api/pith-number/QZBVXM7HRRLPKUNZH5OF5VJJEL/graph.json","fetch_events":"https://pith.science/api/pith-number/QZBVXM7HRRLPKUNZH5OF5VJJEL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QZBVXM7HRRLPKUNZH5OF5VJJEL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QZBVXM7HRRLPKUNZH5OF5VJJEL/action/storage_attestation","attest_author":"https://pith.science/pith/QZBVXM7HRRLPKUNZH5OF5VJJEL/action/author_attestation","sign_citation":"https://pith.science/pith/QZBVXM7HRRLPKUNZH5OF5VJJEL/action/citation_signature","submit_replication":"https://pith.science/pith/QZBVXM7HRRLPKUNZH5OF5VJJEL/action/replication_record"}},"created_at":"2026-07-05T01:05:38.197212+00:00","updated_at":"2026-07-05T01:05:38.197212+00:00"}