{"id":"W2098103715","doi":"10.1109/tmtt.2008.919078","title":"Neural Network Inverse Modeling and Applications to Microwave Filter Design","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Microwave Theory and Techniques","topic":"Microwave Engineering and Waveguides","field":"Engineering","cited_by":331,"is_retracted":false,"has_abstract":true,"ca_institutions":"COM DEV International; Ontario Tech University; Carleton University","funders":"","keywords":"Inverse; Artificial neural network; Inverse filter; Computer science; Inverse problem; Filter (signal processing); Data modeling; Algorithm; Mathematics; Artificial intelligence","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":[0.0004640562,0.0005533203,0.0004118006,0.0004336493,0.0001895411,0.0004712273,0.0004895001,0.0007069982,0.001611801],"category_scores_gemma":[0.001469761,0.0003199293,0.0004901713,0.0004492555,0.0003549217,0.0007604764,0.0003122084,0.0007105048,0.000370537],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003968023,"about_ca_system_score_gemma":0.0003354774,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002327083,"about_ca_topic_score_gemma":0.002348431,"domain_scores_codex":[0.9998272,0.00005727611,0.00001096054,0.00002861478,0.00006530503,0.00001060852],"domain_scores_gemma":[0.9996963,0.0001677273,0.00003130116,0.00002496903,0.00007428707,0.000005501832],"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":[0.00003917098,0.00002318222,0.0004076434,0.0001279291,0.00005569289,0.00006071544,0.00006536317,0.8270605,0.006914553,0.02872269,0.0007476176,0.1357749],"study_design_scores_gemma":[0.000001829242,0.000009275256,0.00005390286,0.000006100261,0.000005093781,0.00001521063,0.000003570495,0.9920725,0.001409818,0.00525826,0.001160629,0.000003816259],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002045419,0.000286838,0.9964324,0.00008624274,0.00001457549,0.000005590925,0.000009739483,0.0001133918,0.001005728],"genre_scores_gemma":[0.3582864,0.002480406,0.6300882,0.0001534592,0.000113547,0.0001886478,0.0001499201,0.000098483,0.008441008],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002327083,"threshold_uncertainty_score":0.005392015,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02088083918042409,"score_gpt":0.2211592707738541,"score_spread":0.20027843159343,"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."}}