{"id":"W3034398297","doi":"10.2514/6.2020-3152","title":"An efficient application of Bayesian optimization to an industrial MDO framework for aircraft design.","year":2020,"lang":"en","type":"article","venue":"AIAA AVIATION 2020 FORUM","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bombardier (Canada)","funders":"","keywords":"Bayesian optimization; Solver; Set (abstract data type); Bayesian probability; Aviation; Global optimization","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":[0.001664008,0.0008883088,0.0009099373,0.0006799265,0.0004073257,0.0007626819,0.001073996,0.001177019,0.00380687],"category_scores_gemma":[0.004212944,0.000667805,0.00080148,0.0006107361,0.000581116,0.0006950523,0.00149479,0.001467476,0.0007224634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006296359,"about_ca_system_score_gemma":0.001726956,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008315826,"about_ca_topic_score_gemma":0.01099668,"domain_scores_codex":[0.9994745,0.0002563849,0.00001821324,0.00003895809,0.0001746674,0.00003729246],"domain_scores_gemma":[0.9990972,0.00058255,0.00006479881,0.00005403383,0.0001547271,0.00004675008],"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":[0.00002451995,0.00002570919,0.0001952571,0.00005717377,0.00002512859,0.0000235892,0.0000234928,0.9611466,0.0005093812,0.01649794,0.0009864033,0.02048492],"study_design_scores_gemma":[0.000005637489,0.000007745524,0.00003213169,0.000006609265,0.000002966727,0.000004498285,0.000003019839,0.995865,0.00008456495,0.003333605,0.0006520181,0.000002159946],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002614101,0.0001847586,0.9934098,0.0001356991,0.00002421322,0.00005500347,0.00007066129,0.0001931149,0.003312648],"genre_scores_gemma":[0.1928019,0.0003567599,0.8022233,0.0001859438,0.00004912354,0.0005410817,0.00037311,0.0002809057,0.003188009],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008315826,"threshold_uncertainty_score":0.01653486,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0265716939448896,"score_gpt":0.294241189913501,"score_spread":0.2676694959686113,"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."}}