{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001455264,0.0002305802,0.0003798426,0.0002124315,0.0002194657,0.000135025,0.0005304269,0.0001364021,0.00002471547],"category_scores_gemma":[0.001222937,0.000179895,0.0001088189,0.0003154631,0.00002914819,0.001304757,0.000113875,0.0002937366,9.940536e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001065487,"about_ca_system_score_gemma":0.0001585131,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002569759,"about_ca_topic_score_gemma":5.547832e-7,"domain_scores_codex":[0.9980372,0.0002591517,0.0006994893,0.0003432321,0.0003691551,0.0002917292],"domain_scores_gemma":[0.9974157,0.0005938345,0.0007669606,0.0002244673,0.0008220683,0.0001769799],"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":[0.0004216077,0.000193377,0.00001449679,0.00003976767,0.00005586052,0.00004121805,0.001672108,0.8860436,0.006375114,0.006196223,0.00006676307,0.09887984],"study_design_scores_gemma":[0.0009251398,0.000707121,0.00002478668,0.0002147233,0.00002728562,0.0001717023,0.0001127955,0.97735,0.01395732,0.006291757,0.000009342819,0.0002080224],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00007281492,0.0000843675,0.9987181,0.0001145758,0.0004926997,0.0004411883,0.000001875839,0.00006048872,0.00001383713],"genre_scores_gemma":[0.06650437,0.00004649489,0.9332148,0.00007813241,0.00008995557,0.00001738086,0.000001014295,0.00003056156,0.00001723672],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.09867182,"threshold_uncertainty_score":0.7335904,"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."}}