{"id":"W2031907338","doi":"10.1115/1.4003840","title":"Integrating Least Square Support Vector Regression and Mode Pursuing Sampling Optimization for Crashworthiness Design","year":2011,"lang":"en","type":"article","venue":"Journal of Mechanical Design","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; University of Manitoba","funders":"","keywords":"Crashworthiness; Metamodeling; Support vector machine; Kriging; Engineering; Multivariate adaptive regression splines; Computer science; Radial basis function; Artificial neural network; Mathematical optimization; Polynomial regression; Machine learning; Regression analysis; Mathematics; Structural engineering; Finite element method","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":[0.001944457,0.0008232088,0.0007677367,0.0006665699,0.000156722,0.0003827742,0.000614945,0.000653363,0.0005847971],"category_scores_gemma":[0.003894747,0.0003807396,0.0006884588,0.0004636688,0.0003138001,0.0005323493,0.000470127,0.000596239,0.0001905317],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002887006,"about_ca_system_score_gemma":0.0005701407,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001953253,"about_ca_topic_score_gemma":0.001626853,"domain_scores_codex":[0.9991273,0.0004301731,0.00003319891,0.00009278227,0.000283549,0.00003286712],"domain_scores_gemma":[0.9988518,0.0007136901,0.000115757,0.0000822155,0.0002133991,0.00002317038],"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":[0.00009853396,0.00006048011,0.001403715,0.00008162179,0.00006450895,0.0000348994,0.00004625493,0.8666121,0.008911566,0.003953565,0.0002399556,0.1184928],"study_design_scores_gemma":[0.000003490003,0.00004272195,0.000117293,0.000002089606,0.000003959336,0.000006357359,0.000002181342,0.9982735,0.0009328338,0.0004551659,0.0001569497,0.000003448294],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01001504,0.00008844164,0.9895073,0.00002066344,0.000005105224,0.00001093903,0.000005271342,0.0001155558,0.0002317145],"genre_scores_gemma":[0.5214179,0.0002130019,0.4768737,0.00004602032,0.00002836906,0.0001461459,0.00007825766,0.0001017863,0.001094856],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001953253,"threshold_uncertainty_score":0.01028335,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1011670882131426,"score_gpt":0.3205314552529235,"score_spread":0.2193643670397809,"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."}}