{"id":"W2762869155","doi":"10.1109/mwsym.2017.8058665","title":"High-order sensitivity analysis with FDTD and the multi-complex step derivative approximation","year":2017,"lang":"en","type":"article","venue":"","topic":"Electromagnetic Simulation and Numerical Methods","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Finite-difference time-domain method; Parametric statistics; Computation; Sensitivity (control systems); Computer science; Range (aeronautics); Approximation error; Algorithm; Mathematics; Applied mathematics; Mathematical optimization; Electronic engineering; Physics; Engineering; Optics","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":[0.0006907287,0.0005749476,0.0003855152,0.0008036945,0.0003172077,0.0006470048,0.0006827674,0.0005693671,0.00128387],"category_scores_gemma":[0.00236843,0.0004198642,0.0005782987,0.0004493686,0.0006400858,0.0007281858,0.0007451284,0.0009350085,0.0002386163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000787221,"about_ca_system_score_gemma":0.0005750922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002090996,"about_ca_topic_score_gemma":0.001327153,"domain_scores_codex":[0.9995017,0.000100309,0.00001589382,0.00004343481,0.0003074999,0.00003114554],"domain_scores_gemma":[0.999151,0.0005971752,0.00004862214,0.00008987894,0.00009704089,0.00001629068],"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":[0.00005688203,0.00004146571,0.0007179455,0.0002148633,0.00005643743,0.0002160659,0.0001871522,0.8206151,0.04166274,0.07711139,0.0009950299,0.05812491],"study_design_scores_gemma":[0.000002471215,0.000007598685,0.0001437373,0.000008362174,0.00000483382,0.00006099294,0.000006074058,0.9820215,0.007663344,0.007810206,0.002260233,0.00001066866],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004562954,0.0001339285,0.9927015,0.00005548919,0.00001924017,0.00001552187,0.00002720526,0.0002213547,0.002262734],"genre_scores_gemma":[0.4810241,0.0007398361,0.5131415,0.0001228352,0.00004769999,0.0001100237,0.0001065876,0.0002597147,0.004447672],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002090996,"threshold_uncertainty_score":0.005711794,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02116615178517494,"score_gpt":0.273220790407641,"score_spread":0.252054638622466,"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."}}