{"id":"W4200567148","doi":"10.1016/j.jcp.2021.110863","title":"Machine learning and reduced order computation of a friction stir welding model","year":2021,"lang":"en","type":"article","venue":"Journal of Computational Physics","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University; National Research Council Canada; Fields Institute for Research in Mathematical Sciences","funders":"","keywords":"Nonlinear system; Artificial neural network; Partial differential equation; Computation; Model order reduction; Computer science; Applied mathematics; Algorithm; Mathematics; Mathematical optimization; Control theory (sociology); Artificial intelligence; Mathematical analysis","routes":{"ca_aff":true,"ca_fund":false,"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":[0.00008673459,0.00008003745,0.0001781844,0.00004048449,0.00008236987,0.0000256697,0.00002949822,0.00001854005,0.00001892764],"category_scores_gemma":[0.000007312116,0.00007791656,0.00007618298,0.0001877208,0.00002193435,0.0001821668,0.00002088978,0.0002171351,5.344144e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001215161,"about_ca_system_score_gemma":0.0001086755,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002899555,"about_ca_topic_score_gemma":5.938551e-8,"domain_scores_codex":[0.9992591,0.00005142086,0.0003148177,0.00008535024,0.0002175969,0.00007172859],"domain_scores_gemma":[0.9988744,0.00007141455,0.0003917217,0.00002454026,0.0005898747,0.000048094],"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":[0.00002216049,0.00007926754,0.0005501549,0.000009910663,0.00005849015,9.093194e-7,0.0001530765,0.9699492,0.001804005,0.007990286,0.00007634121,0.0193062],"study_design_scores_gemma":[0.0004633661,0.00003744185,0.000319672,0.00004067624,0.0000294107,0.00001422464,0.0000912974,0.907681,0.001285894,0.08992609,0.00004675469,0.00006418674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3529296,0.00009434754,0.6463418,0.0001397149,0.00009671909,0.00002205879,0.000003000144,0.000003831653,0.0003689322],"genre_scores_gemma":[0.9806561,0.00001482199,0.01890771,0.00002051826,0.0003060717,4.541789e-7,0.00002467801,0.000009091026,0.00006058764],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6277264,"threshold_uncertainty_score":0.3177345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02002643048890751,"score_gpt":0.2788107186814834,"score_spread":0.2587842881925759,"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."}}