{"id":"W4241172652","doi":"10.1109/date.2002.998409","title":"Efficient model reduction of linear time-varying systems via compressed transient system function","year":2003,"lang":"en","type":"article","venue":"Proceedings 2002 Design, Automation and Test in Europe Conference and Exhibition","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Krylov subspace; Reduction (mathematics); Linear system; Time domain; Basis function; Transfer function; System of linear equations; Algebraic equation; Basis (linear algebra); Subspace topology; Applied mathematics; Transient (computer programming); Computer science; Algorithm; Mathematics; Control theory (sociology); Nonlinear system; Mathematical analysis; Physics; Geometry; Engineering","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":[0.0002919615,0.0006839178,0.0006077943,0.0002929298,0.0002894654,0.0003791886,0.0005210164,0.0004031436,0.001997945],"category_scores_gemma":[0.0008855985,0.0002368522,0.0005438077,0.0002316639,0.0003705756,0.0007606441,0.0005143012,0.0009208623,0.0004079735],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000329467,"about_ca_system_score_gemma":0.0005507197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002644944,"about_ca_topic_score_gemma":0.00241331,"domain_scores_codex":[0.9997972,0.00004776727,0.000009136126,0.00002801694,0.00009892276,0.00001896181],"domain_scores_gemma":[0.9997389,0.0001390843,0.0000254225,0.00004436178,0.00004440199,0.000007892335],"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":[0.000121659,0.00007164872,0.0002909702,0.0001415507,0.00004058629,0.0001082467,0.0001607599,0.7520612,0.0330851,0.02647649,0.001472288,0.1859695],"study_design_scores_gemma":[0.000003562679,0.00001850478,0.00004087037,0.000002107153,0.000002715538,0.00001587923,0.000006556532,0.9932238,0.003638763,0.00223682,0.0008067169,0.00000377861],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009012408,0.00005486303,0.9894287,0.00005039078,0.00001278009,0.00001871016,0.00002595058,0.0003928975,0.001003301],"genre_scores_gemma":[0.4987892,0.0002784015,0.4950914,0.00009942098,0.00005235811,0.0001882935,0.0004315164,0.0002738021,0.004795628],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002644944,"threshold_uncertainty_score":0.006683826,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.026539812890062,"score_gpt":0.2190260680142483,"score_spread":0.1924862551241863,"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."}}