{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:EHX5F3GY6US2LDUEXIFWHESLRU","short_pith_number":"pith:EHX5F3GY","schema_version":"1.0","canonical_sha256":"21efd2ecd8f525a58e84ba0b63924b8d2c511a062ca90bb5568cc2a4cac5253b","source":{"kind":"arxiv","id":"2607.05843","version":1},"attestation_state":"computed","paper":{"title":"Network Interdependency-Informed Power System Dynamic Trajectory Prediction Utilizing Black-Box Modeling of Inverter-Based Resources","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Hantao Cui, Meng Yue, Sungjoo Chung, Ying Zhang","submitted_at":"2026-07-07T05:04:52Z","abstract_excerpt":"Black-box modeling of inverter-based resources (IBRs) has become essential for real-time grid operation and control in the presence of proprietary electronic control architectures. Existing machine learning (ML)-based online dynamic trajectory prediction approaches using IBR black-box models either significantly accumulate prediction errors when multiple surrogates are simultaneously used or ignore measurement errors, limiting their deployment in practical grids. To address these limitations, this paper proposes a novel network interdependency-informed ML algorithm for online dynamic trajector"},"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":"2607.05843","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SY","submitted_at":"2026-07-07T05:04:52Z","cross_cats_sorted":["cs.SY"],"title_canon_sha256":"1c5e25783826e5314e6a1fd56c686213d25a59866f86471c494eb6c762ae8af4","abstract_canon_sha256":"6a19589d1e77096a7941f5ec2fec8c1c3daa56e3215a3f61108259203c8d9f6f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-08T01:18:47.938482Z","signature_b64":"vB4xzq76iDUZk0yYc61wqi1qS9/lC+PSr6i8WS13DLNpC2by+C6uXL4/f60TGxIacohkfgKTNZ1dFQU5rULSAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"21efd2ecd8f525a58e84ba0b63924b8d2c511a062ca90bb5568cc2a4cac5253b","last_reissued_at":"2026-07-08T01:18:47.937988Z","signature_status":"signed_v1","first_computed_at":"2026-07-08T01:18:47.937988Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Network Interdependency-Informed Power System Dynamic Trajectory Prediction Utilizing Black-Box Modeling of Inverter-Based Resources","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Hantao Cui, Meng Yue, Sungjoo Chung, Ying Zhang","submitted_at":"2026-07-07T05:04:52Z","abstract_excerpt":"Black-box modeling of inverter-based resources (IBRs) has become essential for real-time grid operation and control in the presence of proprietary electronic control architectures. Existing machine learning (ML)-based online dynamic trajectory prediction approaches using IBR black-box models either significantly accumulate prediction errors when multiple surrogates are simultaneously used or ignore measurement errors, limiting their deployment in practical grids. To address these limitations, this paper proposes a novel network interdependency-informed ML algorithm for online dynamic trajector"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.05843","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/2607.05843/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":"2607.05843","created_at":"2026-07-08T01:18:47.938056+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.05843v1","created_at":"2026-07-08T01:18:47.938056+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.05843","created_at":"2026-07-08T01:18:47.938056+00:00"},{"alias_kind":"pith_short_12","alias_value":"EHX5F3GY6US2","created_at":"2026-07-08T01:18:47.938056+00:00"},{"alias_kind":"pith_short_16","alias_value":"EHX5F3GY6US2LDUE","created_at":"2026-07-08T01:18:47.938056+00:00"},{"alias_kind":"pith_short_8","alias_value":"EHX5F3GY","created_at":"2026-07-08T01:18:47.938056+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/EHX5F3GY6US2LDUEXIFWHESLRU","json":"https://pith.science/pith/EHX5F3GY6US2LDUEXIFWHESLRU.json","graph_json":"https://pith.science/api/pith-number/EHX5F3GY6US2LDUEXIFWHESLRU/graph.json","events_json":"https://pith.science/api/pith-number/EHX5F3GY6US2LDUEXIFWHESLRU/events.json","paper":"https://pith.science/paper/EHX5F3GY"},"agent_actions":{"view_html":"https://pith.science/pith/EHX5F3GY6US2LDUEXIFWHESLRU","download_json":"https://pith.science/pith/EHX5F3GY6US2LDUEXIFWHESLRU.json","view_paper":"https://pith.science/paper/EHX5F3GY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.05843&json=true","fetch_graph":"https://pith.science/api/pith-number/EHX5F3GY6US2LDUEXIFWHESLRU/graph.json","fetch_events":"https://pith.science/api/pith-number/EHX5F3GY6US2LDUEXIFWHESLRU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EHX5F3GY6US2LDUEXIFWHESLRU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EHX5F3GY6US2LDUEXIFWHESLRU/action/storage_attestation","attest_author":"https://pith.science/pith/EHX5F3GY6US2LDUEXIFWHESLRU/action/author_attestation","sign_citation":"https://pith.science/pith/EHX5F3GY6US2LDUEXIFWHESLRU/action/citation_signature","submit_replication":"https://pith.science/pith/EHX5F3GY6US2LDUEXIFWHESLRU/action/replication_record"}},"created_at":"2026-07-08T01:18:47.938056+00:00","updated_at":"2026-07-08T01:18:47.938056+00:00"}