{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000190064,0.0002514211,0.000308133,0.0002283209,0.0001803244,0.0003112912,0.000681753,0.000384678,0.001243429],"category_scores_gemma":[0.000583892,0.0002388025,0.0003425884,0.0002316822,0.0002477265,0.0004724147,0.0003057751,0.0003321698,0.0004343308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002683618,"about_ca_system_score_gemma":0.0004001201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001169812,"about_ca_topic_score_gemma":0.001433372,"domain_scores_codex":[0.9998701,0.00002234938,0.000005343011,0.00001514795,0.00007805873,0.000009034101],"domain_scores_gemma":[0.9998877,0.00004766474,0.00001138792,0.00002844322,0.00002069819,0.000003933962],"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":[0.00003815561,0.00002505324,0.000522433,0.00009602973,0.00002316449,0.0001602572,0.0001218894,0.7954603,0.1046193,0.03057123,0.001657826,0.06670432],"study_design_scores_gemma":[0.000005587665,0.00001292471,0.0001009201,0.000003650347,0.000004839689,0.00008117752,0.0000054909,0.985181,0.008706171,0.001443212,0.004450449,0.000004523731],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005318641,0.00004569587,0.9924328,0.0000322736,0.00001261352,0.00001395313,0.00002510139,0.0003264698,0.001792467],"genre_scores_gemma":[0.2619798,0.0002169346,0.7342743,0.00003317805,0.00001453087,0.0001029138,0.0001316917,0.0000942104,0.0031526],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001243429,"threshold_uncertainty_score":0.004159749,"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."}}