{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:7NSUQYYWJDNJWOVG7EMQNGIUVL","short_pith_number":"pith:7NSUQYYW","schema_version":"1.0","canonical_sha256":"fb6548631648da9b3aa6f919069914aae78e2fb7ef8dc9bfb9913fe37acf6eba","source":{"kind":"arxiv","id":"2205.08663","version":2},"attestation_state":"computed","paper":{"title":"Physics-Informed Machine Learning for Modeling Turbulence in Supernovae","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.HE"],"primary_cat":"physics.comp-ph","authors_text":"Chengkun Huang, Chris L. Fryer, Ghanshyam Pilania, Iskandar Sitdikov, Platon I. Karpov, Stan Woosley","submitted_at":"2022-05-17T23:42:28Z","abstract_excerpt":"Turbulence plays an important role in astrophysical phenomena, including core-collapse supernovae (CCSN), but current simulations must rely on subgrid models since direct numerical simulation (DNS) is too expensive. Unfortunately, existing subgrid models are not sufficiently accurate. Recently, Machine Learning (ML) has shown an impressive predictive capability for calculating turbulence closure. We have developed a physics-informed convolutional neural network (CNN) to preserve the realizability condition of Reynolds stress that is necessary for accurate turbulent pressure prediction. The app"},"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":"2205.08663","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.comp-ph","submitted_at":"2022-05-17T23:42:28Z","cross_cats_sorted":["astro-ph.HE"],"title_canon_sha256":"388ff3cefc751a043f922e427a6d12de527feec4303f8b821d43389d6fdbe017","abstract_canon_sha256":"a5bbe9b2c2226ee9a529a2a990591998b9e86797ddf481f4ddcb201a5e6949b7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:20:11.602070Z","signature_b64":"JxH3rE6c45sAuKH42Skvll4HnjnoUhRZq6y2MAoi3lhmYH+XriFUFGPSU3gSEmjnJ6pzadyQIqZrm0ENzT90Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fb6548631648da9b3aa6f919069914aae78e2fb7ef8dc9bfb9913fe37acf6eba","last_reissued_at":"2026-07-05T05:20:11.601576Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:20:11.601576Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Physics-Informed Machine Learning for Modeling Turbulence in Supernovae","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.HE"],"primary_cat":"physics.comp-ph","authors_text":"Chengkun Huang, Chris L. Fryer, Ghanshyam Pilania, Iskandar Sitdikov, Platon I. Karpov, Stan Woosley","submitted_at":"2022-05-17T23:42:28Z","abstract_excerpt":"Turbulence plays an important role in astrophysical phenomena, including core-collapse supernovae (CCSN), but current simulations must rely on subgrid models since direct numerical simulation (DNS) is too expensive. Unfortunately, existing subgrid models are not sufficiently accurate. Recently, Machine Learning (ML) has shown an impressive predictive capability for calculating turbulence closure. We have developed a physics-informed convolutional neural network (CNN) to preserve the realizability condition of Reynolds stress that is necessary for accurate turbulent pressure prediction. The app"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.08663","kind":"arxiv","version":2},"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/2205.08663/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":"2205.08663","created_at":"2026-07-05T05:20:11.601639+00:00"},{"alias_kind":"arxiv_version","alias_value":"2205.08663v2","created_at":"2026-07-05T05:20:11.601639+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.08663","created_at":"2026-07-05T05:20:11.601639+00:00"},{"alias_kind":"pith_short_12","alias_value":"7NSUQYYWJDNJ","created_at":"2026-07-05T05:20:11.601639+00:00"},{"alias_kind":"pith_short_16","alias_value":"7NSUQYYWJDNJWOVG","created_at":"2026-07-05T05:20:11.601639+00:00"},{"alias_kind":"pith_short_8","alias_value":"7NSUQYYW","created_at":"2026-07-05T05:20:11.601639+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/7NSUQYYWJDNJWOVG7EMQNGIUVL","json":"https://pith.science/pith/7NSUQYYWJDNJWOVG7EMQNGIUVL.json","graph_json":"https://pith.science/api/pith-number/7NSUQYYWJDNJWOVG7EMQNGIUVL/graph.json","events_json":"https://pith.science/api/pith-number/7NSUQYYWJDNJWOVG7EMQNGIUVL/events.json","paper":"https://pith.science/paper/7NSUQYYW"},"agent_actions":{"view_html":"https://pith.science/pith/7NSUQYYWJDNJWOVG7EMQNGIUVL","download_json":"https://pith.science/pith/7NSUQYYWJDNJWOVG7EMQNGIUVL.json","view_paper":"https://pith.science/paper/7NSUQYYW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2205.08663&json=true","fetch_graph":"https://pith.science/api/pith-number/7NSUQYYWJDNJWOVG7EMQNGIUVL/graph.json","fetch_events":"https://pith.science/api/pith-number/7NSUQYYWJDNJWOVG7EMQNGIUVL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7NSUQYYWJDNJWOVG7EMQNGIUVL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7NSUQYYWJDNJWOVG7EMQNGIUVL/action/storage_attestation","attest_author":"https://pith.science/pith/7NSUQYYWJDNJWOVG7EMQNGIUVL/action/author_attestation","sign_citation":"https://pith.science/pith/7NSUQYYWJDNJWOVG7EMQNGIUVL/action/citation_signature","submit_replication":"https://pith.science/pith/7NSUQYYWJDNJWOVG7EMQNGIUVL/action/replication_record"}},"created_at":"2026-07-05T05:20:11.601639+00:00","updated_at":"2026-07-05T05:20:11.601639+00:00"}