{"id":"W1842495319","doi":"10.1109/aps.2005.1551262","title":"AMR-FDTD: a dynamically adaptive mesh refinement scheme for the finite-difference time-domain technique","year":2005,"lang":"en","type":"article","venue":"","topic":"Electromagnetic Simulation and Numerical Methods","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Finite-difference time-domain method; Adaptive mesh refinement; Mesh generation; Computer science; Boundary (topology); Grid; Upper and lower bounds; Tree (set theory); Finite difference method; Boundary value problem; Algorithm; Finite element method; Computational science; Topology (electrical circuits); Mathematics; Geometry; Engineering; Structural engineering; Mathematical analysis; Physics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002766148,0.0001610174,0.000162894,0.00004518886,0.0000688342,0.00002117265,0.0001911627,0.00008030883,0.0007815753],"category_scores_gemma":[0.0001017022,0.0001111562,0.00007655583,0.0001781381,0.00003583694,0.0000291217,0.00002751545,0.0001568682,0.00005034869],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007489855,"about_ca_system_score_gemma":0.000013437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000476703,"about_ca_topic_score_gemma":0.000008969489,"domain_scores_codex":[0.9991419,0.00004177632,0.0002348878,0.0001684864,0.0001367362,0.0002761565],"domain_scores_gemma":[0.9986653,0.0009585283,0.00002475376,0.0002385427,0.00004608058,0.00006680631],"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":[0.0001988311,0.0001911088,0.0000468375,0.00005332761,0.0002131018,0.000001729767,0.0002652567,0.02909739,0.5581923,0.03778951,0.007988203,0.3659624],"study_design_scores_gemma":[0.0003583646,0.0002295038,0.0002461334,0.00001141232,0.0000133573,0.000001663288,0.00001689867,0.9667801,0.01185007,0.001957463,0.01834195,0.0001931191],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002923841,0.0001136527,0.9845841,0.001053778,0.00003635839,0.0006789451,0.000005763626,0.0003888689,0.01021473],"genre_scores_gemma":[0.1933142,0.00001994337,0.8022654,0.000454087,0.00008330904,0.0004556525,0.000005671259,0.00003376622,0.003367995],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9376827,"threshold_uncertainty_score":0.8557701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01328701864481039,"score_gpt":0.2583563913410803,"score_spread":0.24506937269627,"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."}}