{"id":"W2083021311","doi":"10.1155/2013/928904","title":"Interpolation of Transonic Flows Using a Proper Orthogonal Decomposition Method","year":2013,"lang":"en","type":"article","venue":"International Journal of Aerospace Engineering","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Transonic; Computational fluid dynamics; Eigenfunction; Point of delivery; Mach number; Interpolation (computer graphics); Mathematics; Airfoil; Proper orthogonal decomposition; Applied mathematics; Linear interpolation; Flow (mathematics); Weighting; Variable (mathematics); Algorithm; Grid; Mathematical optimization; Computer science; Mathematical analysis; Mechanics; Geometry; Aerodynamics; Physics; Acoustics","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":[0.000116663,0.00008869922,0.0001406095,0.0001216715,0.00001477024,0.00003754193,0.000133621,0.00002486962,0.0004289324],"category_scores_gemma":[0.000004709563,0.00007768383,0.0001374958,0.00006759255,0.000007188818,0.0003895721,0.00001536899,0.0001692755,0.000002615563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003361082,"about_ca_system_score_gemma":0.00002817313,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003607595,"about_ca_topic_score_gemma":3.5265e-7,"domain_scores_codex":[0.9992617,0.00001960682,0.0003322948,0.00006916574,0.0002262819,0.0000909136],"domain_scores_gemma":[0.9993449,0.00003071808,0.0002127596,0.00004520934,0.0003136333,0.00005279572],"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":[0.00003882714,0.00006778153,0.001941117,0.000007756375,0.0002376759,0.000001663702,0.0002434998,0.6298867,0.3537552,0.001783046,0.00009659303,0.01194006],"study_design_scores_gemma":[0.00046431,0.00004473768,0.001391062,0.0001610034,0.00002500607,0.00005459151,0.00007629497,0.9749776,0.02225843,0.0002211764,0.0002216272,0.0001041401],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4864049,0.00002318231,0.5128746,0.0001533908,0.0004417699,0.00004171933,0.000001841218,0.000003816433,0.00005478061],"genre_scores_gemma":[0.9382548,0.000003546146,0.06120584,0.00001030676,0.0004855445,0.000002092722,0.000002575671,0.00001148168,0.00002384594],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4518499,"threshold_uncertainty_score":0.4696509,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01005639549141562,"score_gpt":0.2850257831055294,"score_spread":0.2749693876141138,"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."}}