{"id":"W2334303329","doi":"10.2514/6.2008-5968","title":"Toward High-Fidelity Aerostructural Optimization Using a Coupled ADjoint Approach","year":2008,"lang":"en","type":"article","venue":"12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference","topic":"Computational Fluid Dynamics and Aerodynamics","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Adjoint equation; Solver; Sensitivity (control systems); Linear system; Computer science; Block (permutation group theory); Applied mathematics; High fidelity; Mathematical optimization; Computational fluid dynamics; Fidelity; Mathematics; Algorithm; Partial differential equation; Mathematical analysis; Engineering; Electronic engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001716782,0.000376114,0.000562843,0.0003820888,0.0004730613,0.0001461493,0.0001918984,0.0001563838,0.0001604882],"category_scores_gemma":[0.00003261338,0.0003758316,0.0001727629,0.001195751,0.0001405091,0.0004648366,0.0001423853,0.0002100089,0.000001443129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001181601,"about_ca_system_score_gemma":0.00008006283,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001426584,"about_ca_topic_score_gemma":0.00002666304,"domain_scores_codex":[0.9980724,0.00005618047,0.0006452125,0.0005515825,0.0003410079,0.0003335558],"domain_scores_gemma":[0.9988635,0.00006372986,0.0001480181,0.0003069813,0.0004234988,0.000194301],"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":[0.00002146228,0.00004441249,0.003731373,0.00004078639,0.0002767984,0.000006687195,0.0004402407,0.994724,0.000204011,0.0003930796,0.000003294674,0.0001138239],"study_design_scores_gemma":[0.0004950693,0.00002721363,0.01742669,0.00001551731,0.0003468617,0.00002739479,0.0001347508,0.980985,0.00001717806,0.00008459739,6.662551e-7,0.0004390767],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3531055,0.00008995819,0.6462511,0.00003110314,0.00009194676,0.0001704563,0.00004730354,0.0001374019,0.00007530648],"genre_scores_gemma":[0.695769,0.000379037,0.3027569,0.00001166843,0.00004169706,0.00001190848,0.0009794572,0.00002714797,0.00002318317],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3434942,"threshold_uncertainty_score":0.9998693,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02658328982029757,"score_gpt":0.2353990167381506,"score_spread":0.208815726917853,"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."}}