{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:UH266D7AYS6ZGCWOILYKFBY5X2","short_pith_number":"pith:UH266D7A","schema_version":"1.0","canonical_sha256":"a1f5ef0fe0c4bd930ace42f0a2871dbe93956fde9d7f2ee992be02c0beee3007","source":{"kind":"arxiv","id":"1909.01074","version":3},"attestation_state":"computed","paper":{"title":"Learning Physics from Data: a Thermodynamic Interpretation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.stat-mech","nlin.AO"],"primary_cat":"physics.data-an","authors_text":"Beatriz Moya, Elias Cueto, Francisco Chinesta, Martin Sipka, Michal Pavelka, Miroslav Grmela","submitted_at":"2019-09-03T11:37:47Z","abstract_excerpt":"Experimental data bases are typically very large and high dimensional. To learn from them requires to recognize important features (a pattern), often present at scales different to that of the recorded data. Following the experience collected in statistical mechanics and thermodynamics, the process of recognizing the pattern (the learning process) can be seen as a dissipative time evolution driven by entropy from a detailed level of description to less detailed. This is the way thermodynamics enters machine learning. On the other hand, reversible (typically Hamiltonian) evolution is propagatio"},"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":"1909.01074","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.data-an","submitted_at":"2019-09-03T11:37:47Z","cross_cats_sorted":["cond-mat.stat-mech","nlin.AO"],"title_canon_sha256":"5e7577f860d6e4915806a88db5013eb76dc13912d36458113c16c30b8a03862e","abstract_canon_sha256":"37ead0d2c2bb959ac2ae10a2c69fcb97993e1682cd7b6d6c7cce72272ac12a84"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:08:16.762583Z","signature_b64":"z800XVTSz2fk04WcZNrj1YQqNNiC1nFvb5h3WpgClesXhcf5GPrTnuQJ4xzCkCB6l7xmQDybmSa4t4yrogcoCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a1f5ef0fe0c4bd930ace42f0a2871dbe93956fde9d7f2ee992be02c0beee3007","last_reissued_at":"2026-07-05T02:08:16.762137Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:08:16.762137Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Physics from Data: a Thermodynamic Interpretation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.stat-mech","nlin.AO"],"primary_cat":"physics.data-an","authors_text":"Beatriz Moya, Elias Cueto, Francisco Chinesta, Martin Sipka, Michal Pavelka, Miroslav Grmela","submitted_at":"2019-09-03T11:37:47Z","abstract_excerpt":"Experimental data bases are typically very large and high dimensional. To learn from them requires to recognize important features (a pattern), often present at scales different to that of the recorded data. Following the experience collected in statistical mechanics and thermodynamics, the process of recognizing the pattern (the learning process) can be seen as a dissipative time evolution driven by entropy from a detailed level of description to less detailed. This is the way thermodynamics enters machine learning. On the other hand, reversible (typically Hamiltonian) evolution is propagatio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.01074","kind":"arxiv","version":3},"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/1909.01074/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":"1909.01074","created_at":"2026-07-05T02:08:16.762193+00:00"},{"alias_kind":"arxiv_version","alias_value":"1909.01074v3","created_at":"2026-07-05T02:08:16.762193+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.01074","created_at":"2026-07-05T02:08:16.762193+00:00"},{"alias_kind":"pith_short_12","alias_value":"UH266D7AYS6Z","created_at":"2026-07-05T02:08:16.762193+00:00"},{"alias_kind":"pith_short_16","alias_value":"UH266D7AYS6ZGCWO","created_at":"2026-07-05T02:08:16.762193+00:00"},{"alias_kind":"pith_short_8","alias_value":"UH266D7A","created_at":"2026-07-05T02:08:16.762193+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/UH266D7AYS6ZGCWOILYKFBY5X2","json":"https://pith.science/pith/UH266D7AYS6ZGCWOILYKFBY5X2.json","graph_json":"https://pith.science/api/pith-number/UH266D7AYS6ZGCWOILYKFBY5X2/graph.json","events_json":"https://pith.science/api/pith-number/UH266D7AYS6ZGCWOILYKFBY5X2/events.json","paper":"https://pith.science/paper/UH266D7A"},"agent_actions":{"view_html":"https://pith.science/pith/UH266D7AYS6ZGCWOILYKFBY5X2","download_json":"https://pith.science/pith/UH266D7AYS6ZGCWOILYKFBY5X2.json","view_paper":"https://pith.science/paper/UH266D7A","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1909.01074&json=true","fetch_graph":"https://pith.science/api/pith-number/UH266D7AYS6ZGCWOILYKFBY5X2/graph.json","fetch_events":"https://pith.science/api/pith-number/UH266D7AYS6ZGCWOILYKFBY5X2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UH266D7AYS6ZGCWOILYKFBY5X2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UH266D7AYS6ZGCWOILYKFBY5X2/action/storage_attestation","attest_author":"https://pith.science/pith/UH266D7AYS6ZGCWOILYKFBY5X2/action/author_attestation","sign_citation":"https://pith.science/pith/UH266D7AYS6ZGCWOILYKFBY5X2/action/citation_signature","submit_replication":"https://pith.science/pith/UH266D7AYS6ZGCWOILYKFBY5X2/action/replication_record"}},"created_at":"2026-07-05T02:08:16.762193+00:00","updated_at":"2026-07-05T02:08:16.762193+00:00"}