{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:YFSWPA7JHN3PUCPV6IAAP7RXQX","short_pith_number":"pith:YFSWPA7J","schema_version":"1.0","canonical_sha256":"c1656783e93b76fa09f5f20007fe3785f08fc9a7ca85b73ff6862b9a63143594","source":{"kind":"arxiv","id":"2508.18969","version":1},"attestation_state":"computed","paper":{"title":"Deep Learning-Enabled Supercritical Flame Simulation at Detailed Chemistry and Real-Fluid Accuracy Towards Trillion-Cell Scale","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.DC","authors_text":"Guangming Tan, Lijun Liu, Runze Mao, Weile Jia, Zhi X.Chen, Zhuoqiang Guo","submitted_at":"2025-08-26T12:13:17Z","abstract_excerpt":"For decades, supercritical flame simulations incorporating detailed chemistry and real-fluid transport have been limited to millions of cells, constraining the resolved spatial and temporal scales of the physical system. We optimize the supercritical flame simulation software DeepFlame -- which incorporates deep neural networks while retaining the real-fluid mechanical and chemical accuracy -- from three perspectives: parallel computing, computational efficiency, and I/O performance. Our highly optimized DeepFlame achieves supercritical liquid oxygen/methane (LOX/\\ce{CH4}) turbulent combustion"},"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":"2508.18969","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DC","submitted_at":"2025-08-26T12:13:17Z","cross_cats_sorted":[],"title_canon_sha256":"5db5bc6bb3aa68e998d269b2b5e4931bee66d5efe8c0afbfe191970b03f84c27","abstract_canon_sha256":"3696e1069b3a925ee2377baea6263e23ddf1c4ecc2f8b52897104678dae75a00"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:59:34.694638Z","signature_b64":"4wyGnTd4OJftgy1zcFCiN9/r7inMG9QOGwevRaYi2kHlr4sgjfIZ0MNULjpEsxJA9ajQJif6WU8Y0EOYFsMMAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c1656783e93b76fa09f5f20007fe3785f08fc9a7ca85b73ff6862b9a63143594","last_reissued_at":"2026-07-05T11:59:34.694128Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:59:34.694128Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Learning-Enabled Supercritical Flame Simulation at Detailed Chemistry and Real-Fluid Accuracy Towards Trillion-Cell Scale","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.DC","authors_text":"Guangming Tan, Lijun Liu, Runze Mao, Weile Jia, Zhi X.Chen, Zhuoqiang Guo","submitted_at":"2025-08-26T12:13:17Z","abstract_excerpt":"For decades, supercritical flame simulations incorporating detailed chemistry and real-fluid transport have been limited to millions of cells, constraining the resolved spatial and temporal scales of the physical system. We optimize the supercritical flame simulation software DeepFlame -- which incorporates deep neural networks while retaining the real-fluid mechanical and chemical accuracy -- from three perspectives: parallel computing, computational efficiency, and I/O performance. Our highly optimized DeepFlame achieves supercritical liquid oxygen/methane (LOX/\\ce{CH4}) turbulent combustion"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.18969","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/2508.18969/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":"2508.18969","created_at":"2026-07-05T11:59:34.694186+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.18969v1","created_at":"2026-07-05T11:59:34.694186+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.18969","created_at":"2026-07-05T11:59:34.694186+00:00"},{"alias_kind":"pith_short_12","alias_value":"YFSWPA7JHN3P","created_at":"2026-07-05T11:59:34.694186+00:00"},{"alias_kind":"pith_short_16","alias_value":"YFSWPA7JHN3PUCPV","created_at":"2026-07-05T11:59:34.694186+00:00"},{"alias_kind":"pith_short_8","alias_value":"YFSWPA7J","created_at":"2026-07-05T11:59:34.694186+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/YFSWPA7JHN3PUCPV6IAAP7RXQX","json":"https://pith.science/pith/YFSWPA7JHN3PUCPV6IAAP7RXQX.json","graph_json":"https://pith.science/api/pith-number/YFSWPA7JHN3PUCPV6IAAP7RXQX/graph.json","events_json":"https://pith.science/api/pith-number/YFSWPA7JHN3PUCPV6IAAP7RXQX/events.json","paper":"https://pith.science/paper/YFSWPA7J"},"agent_actions":{"view_html":"https://pith.science/pith/YFSWPA7JHN3PUCPV6IAAP7RXQX","download_json":"https://pith.science/pith/YFSWPA7JHN3PUCPV6IAAP7RXQX.json","view_paper":"https://pith.science/paper/YFSWPA7J","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.18969&json=true","fetch_graph":"https://pith.science/api/pith-number/YFSWPA7JHN3PUCPV6IAAP7RXQX/graph.json","fetch_events":"https://pith.science/api/pith-number/YFSWPA7JHN3PUCPV6IAAP7RXQX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YFSWPA7JHN3PUCPV6IAAP7RXQX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YFSWPA7JHN3PUCPV6IAAP7RXQX/action/storage_attestation","attest_author":"https://pith.science/pith/YFSWPA7JHN3PUCPV6IAAP7RXQX/action/author_attestation","sign_citation":"https://pith.science/pith/YFSWPA7JHN3PUCPV6IAAP7RXQX/action/citation_signature","submit_replication":"https://pith.science/pith/YFSWPA7JHN3PUCPV6IAAP7RXQX/action/replication_record"}},"created_at":"2026-07-05T11:59:34.694186+00:00","updated_at":"2026-07-05T11:59:34.694186+00:00"}