{"id":"W2100358079","doi":"10.1109/tmag.2009.2022492","title":"Improved Sequential Optimization Method for High Dimensional Electromagnetic Device Optimization","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Magnetics","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Benchmark (surveying); Dimension (graph theory); Optimization problem; Mathematical optimization; Reduction (mathematics); Engineering optimization; Algorithm; 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":[0.0007586736,0.0008217557,0.0009017276,0.0005644584,0.0003007106,0.0004016433,0.0008183464,0.0006248927,0.002916005],"category_scores_gemma":[0.001165179,0.0003858002,0.0008170448,0.0006939771,0.0003582383,0.0005642946,0.0005939937,0.0006896956,0.000566792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004017082,"about_ca_system_score_gemma":0.0008436454,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001830654,"about_ca_topic_score_gemma":0.002400961,"domain_scores_codex":[0.9995614,0.0001691725,0.00001693823,0.00004072426,0.0001874327,0.00002435326],"domain_scores_gemma":[0.9996544,0.0001745339,0.00002927269,0.00003705157,0.00009094849,0.00001381791],"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":[0.00005502741,0.00003519578,0.0002436333,0.0001217451,0.00006020556,0.00004177395,0.00003181779,0.9016749,0.00555763,0.01300948,0.001937524,0.077231],"study_design_scores_gemma":[0.000005301433,0.0000125568,0.00002658014,0.000001653995,0.000002401823,0.000007578592,0.000001356154,0.9975997,0.0003636819,0.001383698,0.0005932275,0.000002274875],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002210179,0.00008545157,0.9963349,0.000032637,0.00001748519,0.00001456278,0.00001520744,0.0001279679,0.001161581],"genre_scores_gemma":[0.1458102,0.0002408339,0.8486654,0.00008070727,0.00004178761,0.0004048153,0.0001525354,0.0001427478,0.004460853],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002916005,"threshold_uncertainty_score":0.009755015,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01282474916389352,"score_gpt":0.2773621062419217,"score_spread":0.2645373570780282,"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."}}