{"id":"W4399733627","doi":"10.1016/j.jcp.2024.113195","title":"CFD stability improvement using dynamic mode decomposition of solution update vectors","year":2024,"lang":"en","type":"article","venue":"Journal of Computational Physics","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Ansys","keywords":"Dynamic mode decomposition; Stability (learning theory); Decomposition; Computational fluid dynamics; Mode (computer interface); Applied mathematics; Computer science; Mathematics; Control theory (sociology); Mechanics; Physics; Chemistry; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001212507,0.00008966403,0.0001559418,0.00005082922,0.00005219834,0.000034491,0.0000607948,0.00001744123,0.00004722608],"category_scores_gemma":[8.36977e-7,0.00008128976,0.0001757629,0.0001411787,0.00003297231,0.0002965645,0.00001817743,0.0001637269,0.00000200308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008192534,"about_ca_system_score_gemma":0.0001381402,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001301041,"about_ca_topic_score_gemma":2.150568e-7,"domain_scores_codex":[0.999131,0.00003616098,0.000390596,0.00009453794,0.0002548615,0.0000929056],"domain_scores_gemma":[0.9993815,0.00005139328,0.0002503374,0.0000519081,0.0002196174,0.00004527148],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003472537,0.0001414323,0.000110622,0.00003175106,0.0001135381,6.317595e-7,0.00009696985,0.938495,0.02074802,0.009847095,0.00009953108,0.0302807],"study_design_scores_gemma":[0.0001683251,0.00006082066,0.0002255048,0.00006057351,0.00004369401,0.00000345723,0.00002380984,0.8640616,0.005067779,0.1301929,0.0000264894,0.00006502081],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5025529,0.00003490469,0.4969502,0.0000722584,0.0002994333,0.00003714631,0.00001573192,0.00000432771,0.00003304019],"genre_scores_gemma":[0.994155,0.000003033563,0.005370333,0.0000142451,0.0004094715,6.879756e-7,0.00003428073,0.000009707705,0.000003262679],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4916021,"threshold_uncertainty_score":0.33149,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01804761773066472,"score_gpt":0.3307561847198692,"score_spread":0.3127085669892045,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}