{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:H6SMHERRTDX2WHRWOIPU75AJPJ","short_pith_number":"pith:H6SMHERR","schema_version":"1.0","canonical_sha256":"3fa4c3923198efab1e36721f4ff4097a70e1f25725773e5395ab808186213c8b","source":{"kind":"arxiv","id":"2411.04502","version":3},"attestation_state":"computed","paper":{"title":"LESnets (Large-Eddy Simulation nets): Physics-informed neural operator for large-eddy simulation of turbulence","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"physics.flu-dyn","authors_text":"Boyu Fan, Huiyu Yang, Jianchun Wang, Sunan Zhao, Yunpeng Wang, Zhijie Li","submitted_at":"2024-11-07T07:53:01Z","abstract_excerpt":"Acquisition of large datasets for three-dimensional (3D) partial differential equations (PDE) is usually very expensive. Physics-informed neural operator (PINO) eliminates the high costs associated with generation of training datasets, and shows great potential in a variety of partial differential equations. In this work, we employ physics-informed neural operator, encoding the large-eddy simulation (LES) equations directly into the neural operator for simulating three-dimensional incompressible turbulent flows. We develop the LESnets (Large-Eddy Simulation nets) by adding large-eddy simulatio"},"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":"2411.04502","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.flu-dyn","submitted_at":"2024-11-07T07:53:01Z","cross_cats_sorted":[],"title_canon_sha256":"776528cb1cb6b3c83ab976fc06e1fc66230dc311a3e79e6c20ad0324f7f2fe94","abstract_canon_sha256":"1134f65f6017343738f3a3313dd15735c26881f21b4faf46aa90ad58e843f13f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:46:21.361138Z","signature_b64":"EyePJPR9S/zfpmkH2PaeVhAzqBhwoJH888lF8dsDrXY6VQF8yeOwMDhf/VNdYi/MKgcRg/QhyqaUpj8Au34KCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3fa4c3923198efab1e36721f4ff4097a70e1f25725773e5395ab808186213c8b","last_reissued_at":"2026-07-05T10:46:21.360667Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:46:21.360667Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LESnets (Large-Eddy Simulation nets): Physics-informed neural operator for large-eddy simulation of turbulence","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"physics.flu-dyn","authors_text":"Boyu Fan, Huiyu Yang, Jianchun Wang, Sunan Zhao, Yunpeng Wang, Zhijie Li","submitted_at":"2024-11-07T07:53:01Z","abstract_excerpt":"Acquisition of large datasets for three-dimensional (3D) partial differential equations (PDE) is usually very expensive. Physics-informed neural operator (PINO) eliminates the high costs associated with generation of training datasets, and shows great potential in a variety of partial differential equations. In this work, we employ physics-informed neural operator, encoding the large-eddy simulation (LES) equations directly into the neural operator for simulating three-dimensional incompressible turbulent flows. We develop the LESnets (Large-Eddy Simulation nets) by adding large-eddy simulatio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.04502","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/2411.04502/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":"2411.04502","created_at":"2026-07-05T10:46:21.360725+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.04502v3","created_at":"2026-07-05T10:46:21.360725+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.04502","created_at":"2026-07-05T10:46:21.360725+00:00"},{"alias_kind":"pith_short_12","alias_value":"H6SMHERRTDX2","created_at":"2026-07-05T10:46:21.360725+00:00"},{"alias_kind":"pith_short_16","alias_value":"H6SMHERRTDX2WHRW","created_at":"2026-07-05T10:46:21.360725+00:00"},{"alias_kind":"pith_short_8","alias_value":"H6SMHERR","created_at":"2026-07-05T10:46:21.360725+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.23662","citing_title":"Modeling turbulent and self-gravitating fluids with Fourier neural operators","ref_index":22,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/H6SMHERRTDX2WHRWOIPU75AJPJ","json":"https://pith.science/pith/H6SMHERRTDX2WHRWOIPU75AJPJ.json","graph_json":"https://pith.science/api/pith-number/H6SMHERRTDX2WHRWOIPU75AJPJ/graph.json","events_json":"https://pith.science/api/pith-number/H6SMHERRTDX2WHRWOIPU75AJPJ/events.json","paper":"https://pith.science/paper/H6SMHERR"},"agent_actions":{"view_html":"https://pith.science/pith/H6SMHERRTDX2WHRWOIPU75AJPJ","download_json":"https://pith.science/pith/H6SMHERRTDX2WHRWOIPU75AJPJ.json","view_paper":"https://pith.science/paper/H6SMHERR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.04502&json=true","fetch_graph":"https://pith.science/api/pith-number/H6SMHERRTDX2WHRWOIPU75AJPJ/graph.json","fetch_events":"https://pith.science/api/pith-number/H6SMHERRTDX2WHRWOIPU75AJPJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/H6SMHERRTDX2WHRWOIPU75AJPJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/H6SMHERRTDX2WHRWOIPU75AJPJ/action/storage_attestation","attest_author":"https://pith.science/pith/H6SMHERRTDX2WHRWOIPU75AJPJ/action/author_attestation","sign_citation":"https://pith.science/pith/H6SMHERRTDX2WHRWOIPU75AJPJ/action/citation_signature","submit_replication":"https://pith.science/pith/H6SMHERRTDX2WHRWOIPU75AJPJ/action/replication_record"}},"created_at":"2026-07-05T10:46:21.360725+00:00","updated_at":"2026-07-05T10:46:21.360725+00:00"}