{"id":"W2592277728","doi":"","title":"Dynamically adaptive mesh refinement FDTD: A stable and efficient technique for time-domain simulations","year":2006,"lang":"en","type":"article","venue":"","topic":"Electromagnetic Simulation and Numerical Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Finite-difference time-domain method; Interpolation (computer graphics); Extrapolation; Adaptive mesh refinement; Computer science; Computational electromagnetics; Computational science; Stability (learning theory); A priori and a posteriori; Algorithm; Finite difference method; Electromagnetic field; Mathematics; Optics; Mathematical analysis; 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":[0.0001474772,0.0001146592,0.0001378611,0.00006864363,0.00005886763,0.00001766123,0.00004175847,0.0000569461,0.0002107082],"category_scores_gemma":[0.0000216727,0.00010552,0.00003241405,0.0001613176,0.00002176669,0.00002016894,0.00001374497,0.00005781365,0.000004684838],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006497644,"about_ca_system_score_gemma":0.000008607351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001834222,"about_ca_topic_score_gemma":0.000009319201,"domain_scores_codex":[0.9993272,0.00002659375,0.00019954,0.000148052,0.00009040315,0.0002082028],"domain_scores_gemma":[0.9995043,0.0002790853,0.00001742068,0.0001074556,0.00004256119,0.00004920145],"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":[0.00003990785,0.00009400702,0.00003667302,0.00003107421,0.00002495348,6.876515e-7,0.00002957642,0.6023163,0.3716999,0.02059823,0.001199129,0.003929654],"study_design_scores_gemma":[0.0003312634,0.0001573319,0.0003165634,0.000008037571,0.00001196208,0.000001240805,0.000007585568,0.9802704,0.01121101,0.005263696,0.002268451,0.0001524054],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03477416,0.00005789374,0.9535422,0.00009813911,0.0000220096,0.0006356429,0.00001691123,0.0002428574,0.01061015],"genre_scores_gemma":[0.5875744,0.000001083383,0.4113178,0.00003162861,0.00002089108,0.00008839047,0.0000150789,0.00002022765,0.0009305003],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5528002,"threshold_uncertainty_score":0.4302981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006635651395554843,"score_gpt":0.2416566004756394,"score_spread":0.2350209490800846,"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."}}