{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:F254R7S2ROEJDAYVEJHDN7MR3P","short_pith_number":"pith:F254R7S2","schema_version":"1.0","canonical_sha256":"2ebbc8fe5a8b88918315224e36fd91dbfe06794075542ae3afe8cfa9796d82b4","source":{"kind":"arxiv","id":"2401.05972","version":3},"attestation_state":"computed","paper":{"title":"Scientific Machine Learning Based Reduced-Order Models for Plasma Turbulence Simulations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CE","cs.LG","physics.plasm-ph"],"primary_cat":"physics.comp-ph","authors_text":"Constantin Gahr, Frank Jenko, Ionut-Gabriel Farcas","submitted_at":"2024-01-11T15:20:06Z","abstract_excerpt":"This paper investigates non-intrusive Scientific Machine Learning (SciML) Reduced-Order Models (ROMs) for plasma turbulence simulations. In particular, we focus on Operator Inference (OpInf) to build low-cost physics-based ROMs from data for such simulations. As a representative example, we consider the (classical) Hasegawa-Wakatani (HW) equations used for modeling two-dimensional electrostatic drift-wave turbulence. For a comprehensive perspective of the potential of OpInf to construct predictive ROMs, we consider three setups for the HW equations by varying a key parameter, namely the adiaba"},"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":"2401.05972","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.comp-ph","submitted_at":"2024-01-11T15:20:06Z","cross_cats_sorted":["cs.CE","cs.LG","physics.plasm-ph"],"title_canon_sha256":"cd946de8adbc640d87ec4811ad383d35c2404ae6b390dce79ceef1579540fd82","abstract_canon_sha256":"fca249751431f278ba2c63da4f929210925a7d54d0358863b5ba922fa73c0ce1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:37:34.460584Z","signature_b64":"O9pQBHmJbOQnliZREo+SDmsQ7HQOHQYa4cLCKKQCiVypqvwnptPBFAoE4ocr0/o/w+r7zEd2ruOThOCpCNmOCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2ebbc8fe5a8b88918315224e36fd91dbfe06794075542ae3afe8cfa9796d82b4","last_reissued_at":"2026-07-05T09:37:34.460149Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:37:34.460149Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Scientific Machine Learning Based Reduced-Order Models for Plasma Turbulence Simulations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CE","cs.LG","physics.plasm-ph"],"primary_cat":"physics.comp-ph","authors_text":"Constantin Gahr, Frank Jenko, Ionut-Gabriel Farcas","submitted_at":"2024-01-11T15:20:06Z","abstract_excerpt":"This paper investigates non-intrusive Scientific Machine Learning (SciML) Reduced-Order Models (ROMs) for plasma turbulence simulations. In particular, we focus on Operator Inference (OpInf) to build low-cost physics-based ROMs from data for such simulations. As a representative example, we consider the (classical) Hasegawa-Wakatani (HW) equations used for modeling two-dimensional electrostatic drift-wave turbulence. For a comprehensive perspective of the potential of OpInf to construct predictive ROMs, we consider three setups for the HW equations by varying a key parameter, namely the adiaba"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.05972","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/2401.05972/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":"2401.05972","created_at":"2026-07-05T09:37:34.460205+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.05972v3","created_at":"2026-07-05T09:37:34.460205+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.05972","created_at":"2026-07-05T09:37:34.460205+00:00"},{"alias_kind":"pith_short_12","alias_value":"F254R7S2ROEJ","created_at":"2026-07-05T09:37:34.460205+00:00"},{"alias_kind":"pith_short_16","alias_value":"F254R7S2ROEJDAYV","created_at":"2026-07-05T09:37:34.460205+00:00"},{"alias_kind":"pith_short_8","alias_value":"F254R7S2","created_at":"2026-07-05T09:37:34.460205+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.03086","citing_title":"iGENE: A Differentiable Flux-Tube Gyrokinetic Code in TensorFlow","ref_index":73,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/F254R7S2ROEJDAYVEJHDN7MR3P","json":"https://pith.science/pith/F254R7S2ROEJDAYVEJHDN7MR3P.json","graph_json":"https://pith.science/api/pith-number/F254R7S2ROEJDAYVEJHDN7MR3P/graph.json","events_json":"https://pith.science/api/pith-number/F254R7S2ROEJDAYVEJHDN7MR3P/events.json","paper":"https://pith.science/paper/F254R7S2"},"agent_actions":{"view_html":"https://pith.science/pith/F254R7S2ROEJDAYVEJHDN7MR3P","download_json":"https://pith.science/pith/F254R7S2ROEJDAYVEJHDN7MR3P.json","view_paper":"https://pith.science/paper/F254R7S2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.05972&json=true","fetch_graph":"https://pith.science/api/pith-number/F254R7S2ROEJDAYVEJHDN7MR3P/graph.json","fetch_events":"https://pith.science/api/pith-number/F254R7S2ROEJDAYVEJHDN7MR3P/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/F254R7S2ROEJDAYVEJHDN7MR3P/action/timestamp_anchor","attest_storage":"https://pith.science/pith/F254R7S2ROEJDAYVEJHDN7MR3P/action/storage_attestation","attest_author":"https://pith.science/pith/F254R7S2ROEJDAYVEJHDN7MR3P/action/author_attestation","sign_citation":"https://pith.science/pith/F254R7S2ROEJDAYVEJHDN7MR3P/action/citation_signature","submit_replication":"https://pith.science/pith/F254R7S2ROEJDAYVEJHDN7MR3P/action/replication_record"}},"created_at":"2026-07-05T09:37:34.460205+00:00","updated_at":"2026-07-05T09:37:34.460205+00:00"}