{"id":"W4248816089","doi":"10.22215/etd/2018-13493","title":"High Fidelity and Efficient Computations of Dynamic Loads for Multidisciplinary Design Optimization of Flexible Transport Aircraft","year":2018,"lang":"en","type":"dissertation","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; York University","funders":"","keywords":"Metamodeling; Kriging; Airframe; Multidisciplinary design optimization; Reduction (mathematics); Computer science; Mathematical optimization; Modal; Process (computing); High fidelity; Set (abstract data type); Surrogate model; Computation; Engineering; Algorithm; Aerospace engineering; Multidisciplinary approach; Mathematics","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":[0.0005233465,0.0008040024,0.000633117,0.0004038918,0.0004776954,0.0009051649,0.0008934584,0.001139673,0.00457218],"category_scores_gemma":[0.003268878,0.0004803213,0.0004752421,0.0003880489,0.0005754621,0.0009048161,0.001315424,0.001657509,0.00100574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005264909,"about_ca_system_score_gemma":0.001019751,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004125071,"about_ca_topic_score_gemma":0.004609049,"domain_scores_codex":[0.999741,0.00006252395,0.00001039405,0.00002464767,0.0001259433,0.0000354695],"domain_scores_gemma":[0.9993637,0.0003421552,0.00004556869,0.00009141632,0.0001129482,0.00004410919],"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":[0.00004944053,0.0000463355,0.0004108328,0.00007508416,0.00001707749,0.0000404927,0.00005551893,0.9687515,0.002650344,0.007903473,0.001240225,0.01875965],"study_design_scores_gemma":[0.000007118326,0.00000997708,0.00009080558,0.000006224318,0.000001513147,0.000006564554,0.00001119878,0.9963471,0.0006341359,0.002113434,0.0007695557,0.000002330678],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07307717,0.0004308355,0.900455,0.0005653087,0.0001551407,0.00009743687,0.0004160646,0.0007408695,0.02406212],"genre_scores_gemma":[0.7260264,0.0003325419,0.2647917,0.0001390159,0.0001381967,0.0002821113,0.0006922907,0.0006587664,0.006939074],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00457218,"threshold_uncertainty_score":0.01529545,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01508972893667291,"score_gpt":0.2972793039837232,"score_spread":0.2821895750470502,"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."}}