{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:R7M37HM5GZ7C3Y5HT457GC3DFV","short_pith_number":"pith:R7M37HM5","schema_version":"1.0","canonical_sha256":"8fd9bf9d9d367e2de3a79f3bf30b632d68f6e7bf5e173f53992acb278af44f0a","source":{"kind":"arxiv","id":"2011.05987","version":1},"attestation_state":"computed","paper":{"title":"Physics-constrained Deep Learning of Multi-zone Building Thermal Dynamics","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NE","cs.SY","eess.SY"],"primary_cat":"cs.LG","authors_text":"Aaron R. Tuor, Draguna L. Vrabie, Jan Drgona, Vikas Chandan","submitted_at":"2020-11-11T06:39:14Z","abstract_excerpt":"We present a physics-constrained control-oriented deep learning method for modeling building thermal dynamics. The proposed method is based on the systematic encoding of physics-based prior knowledge into a structured recurrent neural architecture. Specifically, our method incorporates structural priors from traditional physics-based building modeling into the neural network thermal dynamics model structure. Further, we leverage penalty methods to provide inequality constraints, thereby bounding predictions within physically realistic and safe operating ranges. Observing that stable eigenvalue"},"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":"2011.05987","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-11-11T06:39:14Z","cross_cats_sorted":["cs.NE","cs.SY","eess.SY"],"title_canon_sha256":"4925bdd2bad7d98dbc2c59427089cfb99a9bd2733e810c06b28473fe38838d12","abstract_canon_sha256":"478306fb744a8892755e8f2e43266d828eaa05e24eb93fb0f3720427d650ff98"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:51:10.766908Z","signature_b64":"ves2LTBcFSGlWv1nm6Hclt8MDDAQkTHg4i7pye+xZO+aLWDE6HPWo/hyt6DuM91laX8WDoYk3WgxpN1kA6BBBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8fd9bf9d9d367e2de3a79f3bf30b632d68f6e7bf5e173f53992acb278af44f0a","last_reissued_at":"2026-07-05T01:51:10.766491Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:51:10.766491Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Physics-constrained Deep Learning of Multi-zone Building Thermal Dynamics","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NE","cs.SY","eess.SY"],"primary_cat":"cs.LG","authors_text":"Aaron R. Tuor, Draguna L. Vrabie, Jan Drgona, Vikas Chandan","submitted_at":"2020-11-11T06:39:14Z","abstract_excerpt":"We present a physics-constrained control-oriented deep learning method for modeling building thermal dynamics. The proposed method is based on the systematic encoding of physics-based prior knowledge into a structured recurrent neural architecture. Specifically, our method incorporates structural priors from traditional physics-based building modeling into the neural network thermal dynamics model structure. Further, we leverage penalty methods to provide inequality constraints, thereby bounding predictions within physically realistic and safe operating ranges. Observing that stable eigenvalue"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2011.05987","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/2011.05987/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":"2011.05987","created_at":"2026-07-05T01:51:10.766550+00:00"},{"alias_kind":"arxiv_version","alias_value":"2011.05987v1","created_at":"2026-07-05T01:51:10.766550+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2011.05987","created_at":"2026-07-05T01:51:10.766550+00:00"},{"alias_kind":"pith_short_12","alias_value":"R7M37HM5GZ7C","created_at":"2026-07-05T01:51:10.766550+00:00"},{"alias_kind":"pith_short_16","alias_value":"R7M37HM5GZ7C3Y5H","created_at":"2026-07-05T01:51:10.766550+00:00"},{"alias_kind":"pith_short_8","alias_value":"R7M37HM5","created_at":"2026-07-05T01:51:10.766550+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/R7M37HM5GZ7C3Y5HT457GC3DFV","json":"https://pith.science/pith/R7M37HM5GZ7C3Y5HT457GC3DFV.json","graph_json":"https://pith.science/api/pith-number/R7M37HM5GZ7C3Y5HT457GC3DFV/graph.json","events_json":"https://pith.science/api/pith-number/R7M37HM5GZ7C3Y5HT457GC3DFV/events.json","paper":"https://pith.science/paper/R7M37HM5"},"agent_actions":{"view_html":"https://pith.science/pith/R7M37HM5GZ7C3Y5HT457GC3DFV","download_json":"https://pith.science/pith/R7M37HM5GZ7C3Y5HT457GC3DFV.json","view_paper":"https://pith.science/paper/R7M37HM5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2011.05987&json=true","fetch_graph":"https://pith.science/api/pith-number/R7M37HM5GZ7C3Y5HT457GC3DFV/graph.json","fetch_events":"https://pith.science/api/pith-number/R7M37HM5GZ7C3Y5HT457GC3DFV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/R7M37HM5GZ7C3Y5HT457GC3DFV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/R7M37HM5GZ7C3Y5HT457GC3DFV/action/storage_attestation","attest_author":"https://pith.science/pith/R7M37HM5GZ7C3Y5HT457GC3DFV/action/author_attestation","sign_citation":"https://pith.science/pith/R7M37HM5GZ7C3Y5HT457GC3DFV/action/citation_signature","submit_replication":"https://pith.science/pith/R7M37HM5GZ7C3Y5HT457GC3DFV/action/replication_record"}},"created_at":"2026-07-05T01:51:10.766550+00:00","updated_at":"2026-07-05T01:51:10.766550+00:00"}