{"id":"W2142446369","doi":"10.1109/20.952596","title":"Iterative solvers for hierarchal vector finite element analysis of microwave problems","year":2001,"lang":"en","type":"article","venue":"IEEE Transactions on Magnetics","topic":"Electromagnetic Simulation and Numerical Methods","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Preconditioner; Conjugate gradient method; Finite element method; Iterative method; Matrix (chemical analysis); Dimension (graph theory); Sparse matrix; Applied mathematics; Microwave; Computer science; Enhanced Data Rates for GSM Evolution; Algorithm; Mathematical analysis; Mathematical optimization; Mathematics; Materials science; Physics; Pure mathematics","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":[0.00009755829,0.0001526049,0.0002579428,0.0003423523,0.00005342575,0.0000147735,0.00008157483,0.0000705668,0.0004213993],"category_scores_gemma":[0.000006865879,0.0001582069,0.0002047839,0.0008510649,0.00004301961,0.00003511472,4.383064e-7,0.0001282906,0.00000429669],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000461093,"about_ca_system_score_gemma":0.00001117993,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005728596,"about_ca_topic_score_gemma":0.00003537056,"domain_scores_codex":[0.9991093,0.00004057985,0.0003150629,0.0001626797,0.0001400709,0.0002323616],"domain_scores_gemma":[0.9992334,0.0003970078,0.00003716632,0.000174888,0.00008258507,0.00007498382],"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":[0.00005024465,0.0001013454,0.00001927739,0.0000288064,0.0004117022,6.835267e-7,0.0005216508,0.9063641,0.03786866,0.00003925891,0.00003810987,0.05455611],"study_design_scores_gemma":[0.0006460338,0.0009306803,0.0002858665,0.00001141674,0.0006401056,7.810209e-7,0.00003706754,0.9291016,0.06532047,0.0001357498,0.002691406,0.000198833],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02389714,0.00009358686,0.9747783,0.00007092564,0.000200903,0.0003214032,0.00006980616,0.00008134679,0.0004866096],"genre_scores_gemma":[0.9553845,0.0001842827,0.04371281,0.00005319652,0.00002013768,0.0000722996,0.00001204077,0.00002473189,0.0005360208],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9314873,"threshold_uncertainty_score":0.6451489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02207654291611684,"score_gpt":0.2692318738080727,"score_spread":0.2471553308919559,"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."}}