{"id":"W2168636007","doi":"10.1109/lmwc.2005.863253","title":"The complementary derivatives method: a second-order accurate interpolation scheme for non-uniform grid in FDTD simulation","year":2006,"lang":"en","type":"article","venue":"IEEE Microwave and Wireless Components Letters","topic":"Electromagnetic Simulation and Numerical Methods","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Finite-difference time-domain method; Grid; Interpolation (computer graphics); Truncation error; Model order reduction; Truncation (statistics); Computer science; Boundary (topology); Finite difference method; Boundary value problem; Reduction (mathematics); Domain (mathematical analysis); Computational complexity theory; Resonator; Computational science; Algorithm; Mathematics; Applied mathematics; Mathematical analysis; Geometry; Optics; Physics; 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":[0.0002630829,0.0001954256,0.0002136228,0.000109809,0.00014751,0.00006885277,0.0001092481,0.00005101879,0.00001212362],"category_scores_gemma":[0.000007937131,0.0001698107,0.00005033758,0.0001985115,0.00005010508,0.0001477918,0.00001963499,0.0001483564,0.000001951545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005515825,"about_ca_system_score_gemma":0.000004725318,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005470033,"about_ca_topic_score_gemma":0.00008844704,"domain_scores_codex":[0.9989126,0.00008679754,0.0003942031,0.0002015371,0.00009538834,0.0003094423],"domain_scores_gemma":[0.9989991,0.000729189,0.00007450984,0.0001243347,0.00003584607,0.0000370805],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005260046,0.00001778733,0.0007304219,0.00003711841,0.00002764439,8.175788e-7,0.0001526792,0.02777938,0.9630594,0.00005047542,0.0005978921,0.00749375],"study_design_scores_gemma":[0.001037937,0.00003425177,0.01433615,0.00003199633,0.000008939318,0.000001786103,0.00003737827,0.9253728,0.05564342,0.0002538327,0.003028485,0.0002130188],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5897536,0.00004000854,0.4093266,0.0003124341,0.0001824061,0.0002922129,0.000009040637,0.00003238111,0.00005135686],"genre_scores_gemma":[0.9298974,0.00001392416,0.06925292,0.0005356204,0.000123656,0.00004396054,0.00008306595,0.00003019344,0.00001928102],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.907416,"threshold_uncertainty_score":0.6924679,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01909362631438413,"score_gpt":0.2852726406032957,"score_spread":0.2661790142889116,"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."}}