{"id":"W2811105005","doi":"10.1145/3197517.3201325","title":"Learning three-dimensional flow for interactive aerodynamic design","year":2018,"lang":"en","type":"article","venue":"ACM Transactions on Graphics","topic":"Computer Graphics and Visualization Techniques","field":"Computer Science","cited_by":106,"is_retracted":false,"has_abstract":true,"ca_institutions":"Autodesk (Canada)","funders":"","keywords":"Parametrization (atmospheric modeling); Aerodynamics; Computer science; Kriging; Nonlinear system; Flow (mathematics); Process (computing); Gaussian process; Algorithm; Mathematical optimization; Gaussian; Artificial intelligence; Mathematics; Machine learning; Geometry; Mechanics; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005695411,0.0008472149,0.0006528937,0.000576515,0.000423587,0.0009107955,0.001043979,0.00128328,0.00394226],"category_scores_gemma":[0.00209323,0.0005621918,0.0008321357,0.000385486,0.0008202451,0.0008414524,0.001369545,0.001435781,0.0009282549],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007587037,"about_ca_system_score_gemma":0.0009550637,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00264807,"about_ca_topic_score_gemma":0.002726512,"domain_scores_codex":[0.9997533,0.00005370731,0.000009138575,0.0000433427,0.0001178504,0.00002262809],"domain_scores_gemma":[0.9993777,0.0003239824,0.00005463012,0.0001120126,0.00009023475,0.0000414629],"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":[0.00003264896,0.00004348906,0.0003476484,0.00005229673,0.00001828216,0.00004497863,0.00006254201,0.9366866,0.007256879,0.006041701,0.001042777,0.04837028],"study_design_scores_gemma":[0.000001934327,0.000005577472,0.00002508621,0.000002078115,8.771377e-7,0.000005456344,0.000002571021,0.9967248,0.0008874665,0.001752744,0.0005891927,0.000002279296],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005458562,0.00005601022,0.992406,0.00008963365,0.00001419374,0.00002378727,0.00003684122,0.001061985,0.0008530229],"genre_scores_gemma":[0.3901438,0.0002637472,0.6055435,0.0001511557,0.00004272093,0.0003554565,0.0003374629,0.0005129189,0.002649217],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00394226,"threshold_uncertainty_score":0.01318812,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03225628685857705,"score_gpt":0.299579726557535,"score_spread":0.2673234396989579,"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."}}