{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:UAVJR466TPF6LX2PVDY77LTSQQ","short_pith_number":"pith:UAVJR466","schema_version":"1.0","canonical_sha256":"a02a98f3de9bcbe5df4fa8f1ffae7284004ed2351d324c804dcef9ccaf85240c","source":{"kind":"arxiv","id":"2103.08377","version":3},"attestation_state":"computed","paper":{"title":"Toward Machine Learned Highly Reduce Kinetic Models For Methane/Air Combustion","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"physics.chem-ph","authors_text":"Gilles Bourque, Mark Kelly, Stephen Dooley","submitted_at":"2021-03-15T13:29:08Z","abstract_excerpt":"Accurate low dimension chemical kinetic models for methane are an essential component in the design of efficient gas turbine combustors. Kinetic models coupled to computational fluid dynamics (CFD) provide quick and efficient ways to test the effect of operating conditions, fuel composition and combustor design compared to physical experiments. However, detailed chemical kinetic models are too computationally expensive for use in CFD. We propose a novel data orientated three-step methodology to produce compact models that replicate a target set of detailed model properties to a high fidelity. "},"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":"2103.08377","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"physics.chem-ph","submitted_at":"2021-03-15T13:29:08Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"ae77a7d475b1dd516e4833b5e3ed785b4dc31e2c63ac7d783ecf61dc635d9b41","abstract_canon_sha256":"4961c5a63881870d379e0cba7226fb22e0e44ef22b43654da94f579fee4aa7ae"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:30:31.907393Z","signature_b64":"+YZ6FPm8Sz1AZgaLXL2gRS5bT3fW6UW9JMmCoztPRrejQq04cDSNLXz+1qEJvbGFx4VJUTFoiXmAIEf7JTS4DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a02a98f3de9bcbe5df4fa8f1ffae7284004ed2351d324c804dcef9ccaf85240c","last_reissued_at":"2026-07-05T04:30:31.906996Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:30:31.906996Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Toward Machine Learned Highly Reduce Kinetic Models For Methane/Air Combustion","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"physics.chem-ph","authors_text":"Gilles Bourque, Mark Kelly, Stephen Dooley","submitted_at":"2021-03-15T13:29:08Z","abstract_excerpt":"Accurate low dimension chemical kinetic models for methane are an essential component in the design of efficient gas turbine combustors. Kinetic models coupled to computational fluid dynamics (CFD) provide quick and efficient ways to test the effect of operating conditions, fuel composition and combustor design compared to physical experiments. However, detailed chemical kinetic models are too computationally expensive for use in CFD. We propose a novel data orientated three-step methodology to produce compact models that replicate a target set of detailed model properties to a high fidelity. "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.08377","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/2103.08377/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":"2103.08377","created_at":"2026-07-05T04:30:31.907052+00:00"},{"alias_kind":"arxiv_version","alias_value":"2103.08377v3","created_at":"2026-07-05T04:30:31.907052+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.08377","created_at":"2026-07-05T04:30:31.907052+00:00"},{"alias_kind":"pith_short_12","alias_value":"UAVJR466TPF6","created_at":"2026-07-05T04:30:31.907052+00:00"},{"alias_kind":"pith_short_16","alias_value":"UAVJR466TPF6LX2P","created_at":"2026-07-05T04:30:31.907052+00:00"},{"alias_kind":"pith_short_8","alias_value":"UAVJR466","created_at":"2026-07-05T04:30:31.907052+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/UAVJR466TPF6LX2PVDY77LTSQQ","json":"https://pith.science/pith/UAVJR466TPF6LX2PVDY77LTSQQ.json","graph_json":"https://pith.science/api/pith-number/UAVJR466TPF6LX2PVDY77LTSQQ/graph.json","events_json":"https://pith.science/api/pith-number/UAVJR466TPF6LX2PVDY77LTSQQ/events.json","paper":"https://pith.science/paper/UAVJR466"},"agent_actions":{"view_html":"https://pith.science/pith/UAVJR466TPF6LX2PVDY77LTSQQ","download_json":"https://pith.science/pith/UAVJR466TPF6LX2PVDY77LTSQQ.json","view_paper":"https://pith.science/paper/UAVJR466","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2103.08377&json=true","fetch_graph":"https://pith.science/api/pith-number/UAVJR466TPF6LX2PVDY77LTSQQ/graph.json","fetch_events":"https://pith.science/api/pith-number/UAVJR466TPF6LX2PVDY77LTSQQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UAVJR466TPF6LX2PVDY77LTSQQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UAVJR466TPF6LX2PVDY77LTSQQ/action/storage_attestation","attest_author":"https://pith.science/pith/UAVJR466TPF6LX2PVDY77LTSQQ/action/author_attestation","sign_citation":"https://pith.science/pith/UAVJR466TPF6LX2PVDY77LTSQQ/action/citation_signature","submit_replication":"https://pith.science/pith/UAVJR466TPF6LX2PVDY77LTSQQ/action/replication_record"}},"created_at":"2026-07-05T04:30:31.907052+00:00","updated_at":"2026-07-05T04:30:31.907052+00:00"}