{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:DWB7YG6OSNISWHV63Q5CINLFK2","short_pith_number":"pith:DWB7YG6O","schema_version":"1.0","canonical_sha256":"1d83fc1bce93512b1ebedc3a24356556803d6189d75f537aaf5d8caa0ac47321","source":{"kind":"arxiv","id":"2104.07875","version":1},"attestation_state":"computed","paper":{"title":"Machine Learning Approaches to Learn HyChem Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"physics.chem-ph","authors_text":"Ji-Woong Park, Julian Zanders, Sili Deng, Weiqi Ji","submitted_at":"2021-04-16T03:49:18Z","abstract_excerpt":"The HyChem approach has recently been proposed for modeling high-temperature combustion of real, multi-component fuels. The approach combines lumped reaction steps for fuel thermal and oxidative pyrolysis with detailed chemistry for the oxidation of the resulting pyrolysis products. However, the approach usually shows substantial discrepancies with experimental data within the Negative Temperature Coefficient (NTC) regime, as the low-temperature chemistry is more fuel-specific than high-temperature chemistry. This paper proposes a machine learning approach to learn the HyChem models that can c"},"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":"2104.07875","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.chem-ph","submitted_at":"2021-04-16T03:49:18Z","cross_cats_sorted":[],"title_canon_sha256":"07b194b4017adf569030fff29a1f9c649e1dfc9d4f18eed25725dcdfb6fee2f2","abstract_canon_sha256":"c393cf68fbd42e23c76a5926b77670e6cfdb7a2a3a12500a34595cec14c831d0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:22:11.072420Z","signature_b64":"EQZNVntcClYnGC51Lp1F9a2UnA2/V0631XLcsLqPcq3tESVOixdnCW6buqv+F+tr71Yd8P9RBDsBtoqyfS5mBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1d83fc1bce93512b1ebedc3a24356556803d6189d75f537aaf5d8caa0ac47321","last_reissued_at":"2026-07-05T07:22:11.071985Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:22:11.071985Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Machine Learning Approaches to Learn HyChem Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"physics.chem-ph","authors_text":"Ji-Woong Park, Julian Zanders, Sili Deng, Weiqi Ji","submitted_at":"2021-04-16T03:49:18Z","abstract_excerpt":"The HyChem approach has recently been proposed for modeling high-temperature combustion of real, multi-component fuels. The approach combines lumped reaction steps for fuel thermal and oxidative pyrolysis with detailed chemistry for the oxidation of the resulting pyrolysis products. However, the approach usually shows substantial discrepancies with experimental data within the Negative Temperature Coefficient (NTC) regime, as the low-temperature chemistry is more fuel-specific than high-temperature chemistry. This paper proposes a machine learning approach to learn the HyChem models that can c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2104.07875","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/2104.07875/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":"2104.07875","created_at":"2026-07-05T07:22:11.072035+00:00"},{"alias_kind":"arxiv_version","alias_value":"2104.07875v1","created_at":"2026-07-05T07:22:11.072035+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2104.07875","created_at":"2026-07-05T07:22:11.072035+00:00"},{"alias_kind":"pith_short_12","alias_value":"DWB7YG6OSNIS","created_at":"2026-07-05T07:22:11.072035+00:00"},{"alias_kind":"pith_short_16","alias_value":"DWB7YG6OSNISWHV6","created_at":"2026-07-05T07:22:11.072035+00:00"},{"alias_kind":"pith_short_8","alias_value":"DWB7YG6O","created_at":"2026-07-05T07:22:11.072035+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/DWB7YG6OSNISWHV63Q5CINLFK2","json":"https://pith.science/pith/DWB7YG6OSNISWHV63Q5CINLFK2.json","graph_json":"https://pith.science/api/pith-number/DWB7YG6OSNISWHV63Q5CINLFK2/graph.json","events_json":"https://pith.science/api/pith-number/DWB7YG6OSNISWHV63Q5CINLFK2/events.json","paper":"https://pith.science/paper/DWB7YG6O"},"agent_actions":{"view_html":"https://pith.science/pith/DWB7YG6OSNISWHV63Q5CINLFK2","download_json":"https://pith.science/pith/DWB7YG6OSNISWHV63Q5CINLFK2.json","view_paper":"https://pith.science/paper/DWB7YG6O","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2104.07875&json=true","fetch_graph":"https://pith.science/api/pith-number/DWB7YG6OSNISWHV63Q5CINLFK2/graph.json","fetch_events":"https://pith.science/api/pith-number/DWB7YG6OSNISWHV63Q5CINLFK2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DWB7YG6OSNISWHV63Q5CINLFK2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DWB7YG6OSNISWHV63Q5CINLFK2/action/storage_attestation","attest_author":"https://pith.science/pith/DWB7YG6OSNISWHV63Q5CINLFK2/action/author_attestation","sign_citation":"https://pith.science/pith/DWB7YG6OSNISWHV63Q5CINLFK2/action/citation_signature","submit_replication":"https://pith.science/pith/DWB7YG6OSNISWHV63Q5CINLFK2/action/replication_record"}},"created_at":"2026-07-05T07:22:11.072035+00:00","updated_at":"2026-07-05T07:22:11.072035+00:00"}