{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003244497,0.0003269987,0.0005480425,0.0003901235,0.000170262,0.00002056893,0.0004302622,0.0002329991,0.00002636792],"category_scores_gemma":[0.00005950905,0.0003318612,0.000130315,0.0005882615,0.0001295468,0.0002709939,0.00005111165,0.0001062669,0.00000121301],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008474366,"about_ca_system_score_gemma":0.0002669933,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005010554,"about_ca_topic_score_gemma":0.00002572795,"domain_scores_codex":[0.9977877,0.00006472582,0.0008521574,0.0007163085,0.0003392133,0.0002399476],"domain_scores_gemma":[0.9969496,0.0002809255,0.0007105881,0.0004652459,0.001506081,0.00008761315],"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":[0.0001035848,0.0002432087,0.000008432816,0.0002235034,0.00005265468,6.086272e-7,0.001599726,0.9920214,0.000206682,0.001519237,0.00001646877,0.00400444],"study_design_scores_gemma":[0.0009683769,0.0003077072,0.001889994,0.0001249635,0.00006296706,0.000001879228,0.0003075045,0.9900075,0.005202508,0.0008215534,0.000002239118,0.0003027894],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002767993,0.0001172001,0.9945589,0.0000296948,0.00060278,0.001564749,0.0001054628,0.0001383976,0.0001147919],"genre_scores_gemma":[0.06533793,0.00003349565,0.9324985,0.000008279579,0.0000156049,0.0001148504,0.0009028661,0.00003825789,0.001050202],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.06256994,"threshold_uncertainty_score":0.9999133,"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."}}