{"id":"W2111463386","doi":"10.1109/tmtt.2005.862660","title":"Efficient modeling of microwave integrated-circuit geometries via a dynamically adaptive mesh Refinement-FDTD technique","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Microwave Theory and Techniques","topic":"Electromagnetic Simulation and Numerical Methods","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Finite-difference time-domain method; Solver; Mesh generation; Cartesian coordinate system; Computer science; Adaptive mesh refinement; Maxwell's equations; Computational science; Microwave; Computational electromagnetics; Electromagnetic field; Grid; Finite difference method; Electromagnetic field solver; Electronic engineering; Finite element method; Mathematics; Geometry; Physics; Engineering; Optics; Mathematical analysis","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006675515,0.000304481,0.0003755132,0.0004672644,0.0001066102,0.00002147114,0.0001327209,0.0002124949,0.00005961524],"category_scores_gemma":[0.00001023578,0.000289027,0.0001349122,0.0005426516,0.0001744342,0.0000516211,0.000002664376,0.0004140809,0.000002927552],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001041987,"about_ca_system_score_gemma":0.00001999428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005455995,"about_ca_topic_score_gemma":0.00001448652,"domain_scores_codex":[0.9985908,0.0001801205,0.0005027836,0.0002897305,0.0001468507,0.0002897537],"domain_scores_gemma":[0.9991838,0.0003157062,0.00006764078,0.0002481659,0.0001213974,0.00006334091],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001470257,0.0001300949,5.486063e-7,0.00004484343,0.00004834439,0.000002159595,0.00006197645,0.02421372,0.8917172,0.004944068,0.00000653516,0.07868347],"study_design_scores_gemma":[0.0001828282,0.0003495928,0.000001420162,0.0001087914,0.00006411361,0.00002479208,0.00005015601,0.06222925,0.912755,0.02390125,0.00005094128,0.0002818601],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07317867,0.0002556634,0.9221431,0.00001184805,0.00005578618,0.0004807565,0.00002562269,0.00080137,0.003047168],"genre_scores_gemma":[0.9231232,0.0000916557,0.07646267,0.00003148819,0.00001494197,0.0001137867,0.000003712947,0.00006060814,0.00009792831],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8499445,"threshold_uncertainty_score":0.9999562,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009292106902232079,"score_gpt":0.2282204265252304,"score_spread":0.2189283196229984,"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."}}