{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:QBMGZVLJRKTCKBJSLFS2KMBQTT","short_pith_number":"pith:QBMGZVLJ","schema_version":"1.0","canonical_sha256":"80586cd5698aa62505325965a530309cc9c84e447eab5f4ba510b0f76284e10d","source":{"kind":"arxiv","id":"2604.07292","version":2},"attestation_state":"computed","paper":{"title":"Graph Neural ODE Digital Twins for Control-Oriented Reactor Thermal-Hydraulic Forecasting Under Partial Observability","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"A graph neural network with neural ODE dynamics forecasts reactor thermal-hydraulic states accurately at locations without sensors and adapts to real data.","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Akzhol Almukhametov, Doyeong Lim, Rui Hu, Yang Liu","submitted_at":"2026-04-08T16:58:14Z","abstract_excerpt":"Real-time supervisory control of advanced reactors requires accurate forecasting of plant-wide thermal-hydraulic states, including locations where physical sensors are unavailable. Meeting this need calls for surrogate models that combine predictive fidelity, millisecond-scale inference, and robustness to partial observability. In this work, we present a physics-informed message-passing Graph Neural Network coupled with a Neural Ordinary Differential Equation (GNN-ODE) to addresses all three requirements simultaneously. We represent the whole system as a directed sensor graph whose edges encod"},"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":true},"canonical_record":{"source":{"id":"2604.07292","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-04-08T16:58:14Z","cross_cats_sorted":[],"title_canon_sha256":"cd9cae3d701855c1ac719452693f9f94b138c3cd8136f67a7908df31cac8e76f","abstract_canon_sha256":"69d29eee70488b4e32d30ec4203eecec2a9b9b5788172c37d0aaebc510d8300a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:05:44.439190Z","signature_b64":"4VlkgoWqH6mv312SMdZLhQRRz24HUM4feaOUPgrMaUonkfx24bm/I4k9Pd2Er+6R+RXdvg2Y3gtyWebZRq4sDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"80586cd5698aa62505325965a530309cc9c84e447eab5f4ba510b0f76284e10d","last_reissued_at":"2026-05-20T00:05:44.438655Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:05:44.438655Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Graph Neural ODE Digital Twins for Control-Oriented Reactor Thermal-Hydraulic Forecasting Under Partial Observability","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"A graph neural network with neural ODE dynamics forecasts reactor thermal-hydraulic states accurately at locations without sensors and adapts to real data.","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Akzhol Almukhametov, Doyeong Lim, Rui Hu, Yang Liu","submitted_at":"2026-04-08T16:58:14Z","abstract_excerpt":"Real-time supervisory control of advanced reactors requires accurate forecasting of plant-wide thermal-hydraulic states, including locations where physical sensors are unavailable. Meeting this need calls for surrogate models that combine predictive fidelity, millisecond-scale inference, and robustness to partial observability. In this work, we present a physics-informed message-passing Graph Neural Network coupled with a Neural Ordinary Differential Equation (GNN-ODE) to addresses all three requirements simultaneously. We represent the whole system as a directed sensor graph whose edges encod"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"The GNN-ODE surrogate achieves an average MAE of 0.91 K at 60 s and 2.18 K at 300 s for uninstrumented nodes, with R² up to 0.995 for missing-node state reconstruction; after fine-tuning on 30 experimental sequences the learned flow-dependent heat-transfer scaling recovers a Reynolds-number exponent consistent with established correlations.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the directed sensor graph and topology-guided initializer faithfully encode the true hydraulic connectivity and that the physics-informed message passing plus Neural ODE can generalize from simulation transients to real experimental data without introducing systematic bias in the recovered constitutive relation.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A GNN-ODE digital twin forecasts reactor thermal-hydraulic states under partial observability, achieving low error on held-out transients and recovering a physical heat-transfer correlation during sim-to-real adaptation.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A graph neural network with neural ODE dynamics forecasts reactor thermal-hydraulic states accurately at locations without sensors and adapts to real data.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"74d7f00bd1701c8bf013fb27a5a452544c0d60ef1dfc0fb48f9f26e6b2950c18"},"source":{"id":"2604.07292","kind":"arxiv","version":2},"verdict":{"id":"f5efb809-a2ac-416d-853b-30cdb3fb96c2","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-10T18:25:01.542534Z","strongest_claim":"The GNN-ODE surrogate achieves an average MAE of 0.91 K at 60 s and 2.18 K at 300 s for uninstrumented nodes, with R² up to 0.995 for missing-node state reconstruction; after fine-tuning on 30 experimental sequences the learned flow-dependent heat-transfer scaling recovers a Reynolds-number exponent consistent with established correlations.","one_line_summary":"A GNN-ODE digital twin forecasts reactor thermal-hydraulic states under partial observability, achieving low error on held-out transients and recovering a physical heat-transfer correlation during sim-to-real adaptation.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the directed sensor graph and topology-guided initializer faithfully encode the true hydraulic connectivity and that the physics-informed message passing plus Neural ODE can generalize from simulation transients to real experimental data without introducing systematic bias in the recovered constitutive relation.","pith_extraction_headline":"A graph neural network with neural ODE dynamics forecasts reactor thermal-hydraulic states accurately at locations without sensors and adapts to real data."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.07292/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":2,"snapshot_sha256":"c9b150c366d480179cb032223bd6576c1b587cff8cd56a76a94fb782dbb9d8cb"},"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":"2604.07292","created_at":"2026-05-20T00:05:44.438746+00:00"},{"alias_kind":"arxiv_version","alias_value":"2604.07292v2","created_at":"2026-05-20T00:05:44.438746+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.07292","created_at":"2026-05-20T00:05:44.438746+00:00"},{"alias_kind":"pith_short_12","alias_value":"QBMGZVLJRKTC","created_at":"2026-05-20T00:05:44.438746+00:00"},{"alias_kind":"pith_short_16","alias_value":"QBMGZVLJRKTCKBJS","created_at":"2026-05-20T00:05:44.438746+00:00"},{"alias_kind":"pith_short_8","alias_value":"QBMGZVLJ","created_at":"2026-05-20T00:05:44.438746+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":2,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QBMGZVLJRKTCKBJSLFS2KMBQTT","json":"https://pith.science/pith/QBMGZVLJRKTCKBJSLFS2KMBQTT.json","graph_json":"https://pith.science/api/pith-number/QBMGZVLJRKTCKBJSLFS2KMBQTT/graph.json","events_json":"https://pith.science/api/pith-number/QBMGZVLJRKTCKBJSLFS2KMBQTT/events.json","paper":"https://pith.science/paper/QBMGZVLJ"},"agent_actions":{"view_html":"https://pith.science/pith/QBMGZVLJRKTCKBJSLFS2KMBQTT","download_json":"https://pith.science/pith/QBMGZVLJRKTCKBJSLFS2KMBQTT.json","view_paper":"https://pith.science/paper/QBMGZVLJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2604.07292&json=true","fetch_graph":"https://pith.science/api/pith-number/QBMGZVLJRKTCKBJSLFS2KMBQTT/graph.json","fetch_events":"https://pith.science/api/pith-number/QBMGZVLJRKTCKBJSLFS2KMBQTT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QBMGZVLJRKTCKBJSLFS2KMBQTT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QBMGZVLJRKTCKBJSLFS2KMBQTT/action/storage_attestation","attest_author":"https://pith.science/pith/QBMGZVLJRKTCKBJSLFS2KMBQTT/action/author_attestation","sign_citation":"https://pith.science/pith/QBMGZVLJRKTCKBJSLFS2KMBQTT/action/citation_signature","submit_replication":"https://pith.science/pith/QBMGZVLJRKTCKBJSLFS2KMBQTT/action/replication_record"}},"created_at":"2026-05-20T00:05:44.438746+00:00","updated_at":"2026-05-20T00:05:44.438746+00:00"}