{"id":"W2100588198","doi":"10.2514/1.33836","title":"Mesh Movement for a Discrete-Adjoint Newton-Krylov Algorithm for Aerodynamic Optimization","year":2008,"lang":"en","type":"article","venue":"AIAA Journal","topic":"Computational Fluid Dynamics and Aerodynamics","field":"Engineering","cited_by":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"University of Toronto; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Aerodynamics; Computer science; Computational fluid dynamics; Algorithm; Mathematics; Applied mathematics; Mathematical optimization; Physics; Mechanics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007099244,0.0004795751,0.0005199912,0.0003319457,0.0005090936,0.0006173854,0.0009709496,0.0008561483,0.005441517],"category_scores_gemma":[0.001536523,0.0003199561,0.0005153293,0.0003360666,0.0004694343,0.000550746,0.0009627247,0.001265614,0.001382689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005285569,"about_ca_system_score_gemma":0.000931206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002692319,"about_ca_topic_score_gemma":0.003056693,"domain_scores_codex":[0.9996997,0.00008014614,0.00001318398,0.00003356644,0.0001492268,0.00002411542],"domain_scores_gemma":[0.9996552,0.0001848038,0.00002305853,0.00003842621,0.00008128549,0.00001720458],"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":[0.0001119118,0.00008007016,0.0007644185,0.0001375664,0.00002822891,0.0001336442,0.0001393882,0.7504271,0.01066448,0.07340532,0.004209082,0.1598988],"study_design_scores_gemma":[0.0000141701,0.0000179234,0.00004743802,0.000006283088,0.000002176079,0.00001283691,0.000005591146,0.9935443,0.0006200555,0.003189092,0.002536157,0.000003906291],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00252689,0.00003610057,0.995496,0.00004655656,0.00003306611,0.00003467787,0.00001838506,0.0002784576,0.001529806],"genre_scores_gemma":[0.07460462,0.0000612666,0.9206001,0.00005485673,0.00002354117,0.0002901899,0.0001023041,0.0002161126,0.004047041],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005441517,"threshold_uncertainty_score":0.01820368,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008432032091585266,"score_gpt":0.2152723267010631,"score_spread":0.2068402946094778,"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."}}