{"id":"W3204887505","doi":"10.1109/temc.2021.3139910","title":"An Unconditionally Stable Conformal LOD-FDTD Method for Curved PEC Objects and its Application to EMC Problems","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Electromagnetic Compatibility","topic":"Electromagnetic Simulation and Numerical Methods","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Finite-difference time-domain method; Conformal map; Computer science; Computational electromagnetics; Mathematics; Computational science; Algorithm; Electronic engineering; Mathematical analysis; Physics; Electromagnetic field; Engineering; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007732676,0.0002765737,0.0003491046,0.0001979908,0.0004613361,0.00004282835,0.00021669,0.00006716472,0.0005761857],"category_scores_gemma":[0.00001698542,0.0003210903,0.00009243158,0.0006113967,0.00002637456,0.0001463919,0.0000029916,0.0003862632,0.000007326354],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002422456,"about_ca_system_score_gemma":0.00007959466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006131092,"about_ca_topic_score_gemma":0.0001436462,"domain_scores_codex":[0.9978542,0.000308213,0.0004735811,0.0005342846,0.0003370083,0.0004926661],"domain_scores_gemma":[0.9986221,0.0005249766,0.00005828351,0.0003987802,0.0001365664,0.0002593486],"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":[0.0001865804,0.0004222945,0.00001896177,0.0001152436,0.0000434658,2.572673e-7,0.0004835188,0.6380769,0.3202284,0.0002957043,0.0000582379,0.04007043],"study_design_scores_gemma":[0.001112737,0.004805641,0.002498663,0.000005680258,0.00006465571,0.00001631442,0.00006594136,0.9380878,0.05036618,0.001793329,0.0007565545,0.0004264883],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3063785,0.00007419172,0.6910289,0.0001010973,0.0001429866,0.001645641,0.0001351526,0.0003009093,0.0001925512],"genre_scores_gemma":[0.9581834,0.00000759465,0.03967169,0.0002142753,0.00002818106,0.001670197,0.00005417318,0.00004682079,0.0001236676],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6518049,"threshold_uncertainty_score":0.9999241,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01410888749146565,"score_gpt":0.2848658600794182,"score_spread":0.2707569725879526,"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."}}