{"id":"W2047679457","doi":"10.1109/tap.2014.2365047","title":"Algebraic Multigrid Combined With Domain Decomposition for the Finite Element Analysis of Large Scattering Problems","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Antennas and Propagation","topic":"Electromagnetic Simulation and Numerical Methods","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Multigrid method; Domain decomposition methods; Finite element method; Scattering; Algebraic equation; Matrix (chemical analysis); Matrix decomposition; Mathematical analysis; Plane wave; Applied mathematics; Mathematics; Computer science; Physics; Partial differential equation; Optics; Materials science; Eigenvalues and eigenvectors; Quantum mechanics","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":[0.0002669223,0.00009794092,0.0001550155,0.0001442707,0.0001302823,0.00002006285,0.00003894698,0.00003128473,0.00002442526],"category_scores_gemma":[0.000003183614,0.0000675132,0.00006126457,0.0003668102,0.00002761209,0.00005097869,3.595517e-7,0.00006916421,5.581261e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001421268,"about_ca_system_score_gemma":0.00000308619,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006554943,"about_ca_topic_score_gemma":0.0000584569,"domain_scores_codex":[0.9994196,0.00004977244,0.000193978,0.0001166447,0.00009465261,0.0001253786],"domain_scores_gemma":[0.9994572,0.0003059198,0.00004513048,0.0001096219,0.00005070599,0.00003145463],"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":[0.000255508,0.0001242993,0.0001606792,0.0001339576,0.0005827147,9.744124e-8,0.0005395605,0.8525224,0.07969496,0.0003260978,0.000005435469,0.06565429],"study_design_scores_gemma":[0.0008167682,0.0005986642,0.002770293,0.00002905909,0.0002582216,5.610608e-7,0.00003409231,0.9758562,0.0193014,0.0001094025,0.0001308315,0.00009446697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1200999,0.00002651938,0.8791784,0.0001751446,0.00005740349,0.000378693,0.00001330791,0.00004984345,0.00002074492],"genre_scores_gemma":[0.9912797,0.00003918377,0.008449445,0.00006971482,0.00001026973,0.0001100892,0.00001342534,0.00001309435,0.00001508886],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8711798,"threshold_uncertainty_score":0.2753109,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009861001850567663,"score_gpt":0.2541583140494351,"score_spread":0.2442973121988675,"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."}}