{"id":"W2291284919","doi":"10.1109/tmag.2015.2487969","title":"On the Role of Robustness in Multi-Objective Robust Optimization: Application to an IPM Motor Design Problem","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Magnetics","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Robustness (evolution); Computer science; Robust optimization; Mathematical optimization; Optimization problem; Control theory (sociology); Control engineering; Mathematics; Artificial intelligence; Engineering; Algorithm","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003657391,0.001193883,0.0009450116,0.0005970963,0.0005144878,0.0009961477,0.0005851592,0.001395164,0.001707496],"category_scores_gemma":[0.008923083,0.0003352197,0.0009403197,0.0004970808,0.001429426,0.0015109,0.001334589,0.001461527,0.000222432],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003477775,"about_ca_system_score_gemma":0.0003334765,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007663081,"about_ca_topic_score_gemma":0.0004661459,"domain_scores_codex":[0.9987075,0.0008075921,0.00005993281,0.0001198317,0.0002511078,0.00005404754],"domain_scores_gemma":[0.9910404,0.008082793,0.0003155544,0.000171655,0.0003349715,0.00005468254],"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":[0.00008960157,0.00003643941,0.0002969199,0.0002406003,0.00005406014,0.0002070443,0.00008436022,0.9219387,0.003504076,0.03379478,0.0004329097,0.03932039],"study_design_scores_gemma":[0.000008221585,0.0001160532,0.000154801,0.00003204629,0.00001876876,0.00004739085,0.00001801853,0.9792345,0.001837948,0.01749005,0.001022887,0.0000192751],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007044395,0.0004813006,0.9887416,0.0003367588,0.0000279832,0.00002401446,0.00000963431,0.00004069855,0.00329352],"genre_scores_gemma":[0.6116846,0.002197238,0.3820435,0.0003230326,0.0002616995,0.0001748303,0.0000452857,0.0001647571,0.003105048],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003657391,"threshold_uncertainty_score":0.01934236,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03609294010573284,"score_gpt":0.2628985279704792,"score_spread":0.2268055878647464,"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."}}