{"id":"W1931198620","doi":"10.1109/usnc-ursi.2015.7303430","title":"A single-level implementation for a fast direct method of moments solver on electrically large scattering problems using a GPU based Reduced Singular Value Decomposition block LU factorization","year":2015,"lang":"en","type":"article","venue":"","topic":"Electromagnetic Scattering and Analysis","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Singular value decomposition; Solver; LU decomposition; Matrix decomposition; Block (permutation group theory); Computer science; Factorization; Method of moments (probability theory); Computational science; Parallel computing; Algorithm; Mathematics; Mathematical optimization; Physics; Eigenvalues and eigenvectors; Combinatorics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002681288,0.0007588078,0.0006417331,0.000379794,0.0004530963,0.0008642536,0.001265955,0.000772327,0.015971],"category_scores_gemma":[0.000893971,0.0003597819,0.0006493788,0.0004447054,0.0002422329,0.0005498386,0.0007784382,0.0009901753,0.005655729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003957204,"about_ca_system_score_gemma":0.001053929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003910672,"about_ca_topic_score_gemma":0.006110142,"domain_scores_codex":[0.999774,0.00002505824,0.00001286502,0.0000271318,0.0001296659,0.00003128116],"domain_scores_gemma":[0.9997229,0.00007114357,0.00001406286,0.00005089093,0.000113395,0.00002760071],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005104603,0.0004172228,0.003444794,0.0004818233,0.0001812747,0.001061994,0.0006454978,0.313209,0.1041996,0.05830487,0.06187727,0.4556662],"study_design_scores_gemma":[0.00007392142,0.00005620166,0.0001891813,0.00002172078,0.000009808845,0.0001239653,0.00003442409,0.9700402,0.008044983,0.002997005,0.01839221,0.00001644713],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01160041,0.0001340527,0.9642897,0.0001729302,0.00009172664,0.000109148,0.0002278131,0.007284499,0.01608966],"genre_scores_gemma":[0.1129974,0.0001764539,0.8718273,0.0001736592,0.00003397734,0.0003099704,0.0009262623,0.00116327,0.0123917],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.015971,"threshold_uncertainty_score":0.05342835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04727261356063982,"score_gpt":0.3394385870566162,"score_spread":0.2921659734959764,"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."}}