{"id":"W4285118183","doi":"10.1109/tap.2022.3177549","title":"Accelerated IE-GSTC Solver for Large-Scale Metasurface Field Scattering Problems Using Fast Multipole Method (FMM)","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Antennas and Propagation","topic":"Advanced Antenna and Metasurface Technologies","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Ministère de la Défense Nationale","keywords":"Solver; Multipole expansion; Fast multipole method; Computation; Field (mathematics); Algorithm; Computer science; Scale (ratio); Mathematics; Applied mathematics; Mathematical analysis; Physics; Mathematical optimization; Pure mathematics; Quantum mechanics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002680979,0.0002037401,0.0002329442,0.0001554515,0.0005587692,0.00004628292,0.0001065467,0.00007600326,0.00005131013],"category_scores_gemma":[0.000005412097,0.0002020493,0.00008836887,0.0002850904,0.00002255094,0.0002477765,0.000004570145,0.0003040947,0.000002106446],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006749502,"about_ca_system_score_gemma":0.00001198503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002106955,"about_ca_topic_score_gemma":0.00003323273,"domain_scores_codex":[0.9989703,0.00004049631,0.0002502291,0.0002835558,0.000139311,0.0003161507],"domain_scores_gemma":[0.9995932,0.0000695792,0.00005598271,0.0001768072,0.00005756285,0.00004686687],"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":[0.00004229171,0.00005320148,0.000008050835,0.00008717272,0.00004972421,9.469077e-7,0.0003768005,0.4024964,0.5695526,0.0000157004,0.00002063671,0.02729646],"study_design_scores_gemma":[0.0005393653,0.0001351389,0.00001165826,0.00003028155,0.00005280792,0.00001416992,0.0009085246,0.6874609,0.3086306,0.00009109615,0.001907562,0.0002178782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09278373,0.0002424436,0.9051094,0.0001903787,0.0004194656,0.0006530308,0.0001142081,0.0004646229,0.00002274395],"genre_scores_gemma":[0.9461833,0.000149139,0.05306443,0.00008398807,0.00001510269,0.0002624015,0.00001133936,0.00004750886,0.0001828169],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8533996,"threshold_uncertainty_score":0.8239332,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03704528776014552,"score_gpt":0.2813169972653377,"score_spread":0.2442717095051922,"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."}}