{"id":"W2950384562","doi":"10.2514/6.2019-3197","title":"Aerodynamic Shape Optimization for the NURBS-Enhanced Discontinuous Galerkin Method","year":2019,"lang":"en","type":"article","venue":"AIAA Aviation 2019 Forum","topic":"Numerical methods in engineering","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Aerodynamics; Shape optimization; Computer science; Discontinuous Galerkin method; Galerkin method; Mathematical optimization; Aerospace engineering; Finite element method; Mathematics; Structural engineering; Engineering","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":[0.0004067939,0.0002084386,0.0002368759,0.0000810678,0.0000744854,0.00005649467,0.000259533,0.0001126361,0.0002703414],"category_scores_gemma":[0.0001417976,0.0001713623,0.0001159902,0.0002611917,0.00001315147,0.0002728134,0.00003557303,0.0001728783,0.0001101345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001077402,"about_ca_system_score_gemma":0.000009744016,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006390948,"about_ca_topic_score_gemma":0.000003262537,"domain_scores_codex":[0.9988357,0.00004430658,0.0003111171,0.0002430249,0.0001759927,0.0003898814],"domain_scores_gemma":[0.998812,0.0006033813,0.00007667985,0.000395121,0.00006414035,0.00004866223],"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":[0.000009195504,0.000008051857,0.00009358479,0.0000368334,0.00004498875,7.198244e-8,0.00006843424,0.922577,0.01386768,0.0009658677,0.000788983,0.06153934],"study_design_scores_gemma":[0.0003909993,0.00004069636,0.001029881,0.00002354072,0.00002971921,0.00000145078,0.00007718025,0.9914758,0.003273567,0.0004110236,0.003007298,0.0002388745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006067999,0.0002409095,0.9891578,0.0003052657,0.002136349,0.0008942594,0.00002391928,0.0004025544,0.0007709423],"genre_scores_gemma":[0.5843004,0.00005741013,0.4141751,0.0001230753,0.0001453853,0.0001986306,0.00006066598,0.0001116717,0.0008277008],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5782324,"threshold_uncertainty_score":0.6987951,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005135906411369676,"score_gpt":0.2473399357521204,"score_spread":0.2422040293407507,"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."}}