{"id":"W2289661767","doi":"10.1109/nemo.2015.7415069","title":"A novel algorithm for efficient simulation of nonlinear transmission lines for RF applications via model order reduction","year":2015,"lang":"en","type":"article","venue":"","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Reduction (mathematics); Nonlinear system; Embedding; Model order reduction; Electric power transmission; Computer science; Transmission (telecommunications); Algorithm; Electronic engineering; Mathematical optimization; Mathematics; Engineering; Telecommunications","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":[0.00008883934,0.00009532817,0.0001232558,0.00004227911,0.00007290218,0.00000921098,0.00005106611,0.00004268417,0.00001153435],"category_scores_gemma":[0.000001568068,0.00008079348,0.00009214676,0.0001271624,0.00001858748,0.00005477068,0.000006683471,0.00003914957,0.000001023574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001116659,"about_ca_system_score_gemma":0.00005156874,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001506763,"about_ca_topic_score_gemma":1.335059e-7,"domain_scores_codex":[0.9993638,0.000004019914,0.0002342222,0.0001875289,0.00009283501,0.0001175539],"domain_scores_gemma":[0.9992689,0.00002852039,0.0000877437,0.0001121407,0.0004241375,0.00007849328],"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":[0.00002944727,0.0001944492,4.005462e-7,0.000006718059,0.000008790501,6.44848e-10,0.00004958119,0.7484027,0.003938605,0.0006325229,0.0001211902,0.2466157],"study_design_scores_gemma":[0.0009251716,0.00004729932,2.343743e-7,0.000005705184,0.00002806381,2.422945e-7,0.00006435312,0.9833618,0.007969593,0.001853936,0.005652082,0.00009146112],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001509004,0.00001085287,0.9968442,0.000163993,0.00008809419,0.001060026,0.00006363889,0.00002863519,0.0002315701],"genre_scores_gemma":[0.4317235,0.000001083006,0.5658692,0.00001399972,0.0006282591,0.0003723399,0.0002527325,0.00002075488,0.001118109],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.430975,"threshold_uncertainty_score":0.3294663,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04351865850926279,"score_gpt":0.3158657499984608,"score_spread":0.272347091489198,"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."}}