{"id":"W3134895301","doi":"10.1109/tap.2021.3061119","title":"An Accelerated Surface Integral Equation Method for the Electromagnetic Modeling of Dielectric and Lossy Objects of Arbitrary Conductivity","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Antennas and Propagation","topic":"Electromagnetic Scattering and Analysis","field":"Physics and Astronomy","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; CMC Microsystems; Advanced Micro Devices","keywords":"Integral equation; Lossy compression; Scalability; Range (aeronautics); Dielectric; Boundary value problem; Computer science; Iterative method; Mathematical analysis; Electrical conductor; Mathematics; Surface (topology); Algorithm; Mathematical optimization; Applied mathematics; Geometry; Materials science","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":[0.0003601463,0.0004838412,0.0004670561,0.0003309008,0.0003043091,0.0004754698,0.0008499297,0.0007872596,0.001799089],"category_scores_gemma":[0.000859604,0.0001506458,0.0006818044,0.0004887608,0.0003653526,0.0007270433,0.0006467045,0.000986323,0.0007819426],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002638401,"about_ca_system_score_gemma":0.0007369842,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008571788,"about_ca_topic_score_gemma":0.001100433,"domain_scores_codex":[0.9998353,0.00003553288,0.000005835555,0.00001623898,0.00009784274,0.000009263747],"domain_scores_gemma":[0.9998145,0.00006874061,0.00001882358,0.00002905743,0.00005759301,0.00001132914],"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":[0.00006446715,0.0001005263,0.0009271179,0.0002714196,0.00006031367,0.0004080233,0.0002994711,0.5205447,0.06947759,0.1924634,0.00598406,0.209399],"study_design_scores_gemma":[0.000005768879,0.00001429525,0.00006582717,0.00000777473,0.000004718761,0.00009584065,0.000009262078,0.985469,0.002172531,0.005432077,0.006715864,0.00000699135],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002771118,0.0001272674,0.9951122,0.00006509942,0.00004517961,0.00002047612,0.00002339334,0.000160324,0.001674931],"genre_scores_gemma":[0.09255436,0.0005631183,0.900358,0.0000937238,0.00007155122,0.0001900917,0.0001473771,0.0001752045,0.005846567],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001799089,"threshold_uncertainty_score":0.006018519,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03084489303983659,"score_gpt":0.2873118630673663,"score_spread":0.2564669700275297,"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."}}