{"id":"W2997890789","doi":"10.1109/tmtt.2019.2955117","title":"EM-Centric Multiphysics Optimization of Microwave Components Using Parallel Computational Approach","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Microwave Theory and Techniques","topic":"Microwave Engineering and Waveguides","field":"Engineering","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Science Foundation of Beijing Municipality; China Postdoctoral Science Foundation; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Multiphysics; Surrogate model; Space mapping; Computer science; Computational electromagnetics; Microwave; Mathematical optimization; Trust region; Electromagnetics; Electronic engineering; Algorithm; Finite element method; Mathematics; Engineering; Electromagnetic field; Physics","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":[0.0005351678,0.0008805543,0.0007714207,0.0004607583,0.0004341572,0.0006375961,0.0007002448,0.0007698144,0.001909374],"category_scores_gemma":[0.001003833,0.0005101779,0.0008169844,0.0004816518,0.0005455667,0.000719366,0.0006905948,0.0008858538,0.0002892565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005662927,"about_ca_system_score_gemma":0.001062379,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002463637,"about_ca_topic_score_gemma":0.002834202,"domain_scores_codex":[0.9998337,0.00005205587,0.000006220662,0.00002650493,0.0000625986,0.00001894605],"domain_scores_gemma":[0.999662,0.0001667499,0.00004710936,0.00004230631,0.00006325313,0.00001856396],"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":[0.00001594966,0.00001629896,0.0001558816,0.00002552531,0.0000156105,0.00002154978,0.00001145571,0.988754,0.00144364,0.002629677,0.000139129,0.006771307],"study_design_scores_gemma":[0.000002637874,0.00000542228,0.0000207603,8.496274e-7,0.000001354074,0.000003084796,0.000001876075,0.9989021,0.000225841,0.0006972026,0.0001378696,0.000001101321],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.015657,0.00013688,0.980225,0.0001327013,0.00002306113,0.00003903414,0.00002934268,0.0001798774,0.003577181],"genre_scores_gemma":[0.551498,0.0003081404,0.4424288,0.0001401661,0.00004816305,0.0004010111,0.0001479645,0.000233636,0.004794161],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002463637,"threshold_uncertainty_score":0.006387472,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01161127931677448,"score_gpt":0.213745295036788,"score_spread":0.2021340157200135,"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."}}