{"id":"W3119875199","doi":"10.1137/19m1308669","title":"Tuning Multigrid Methods with Robust Optimization and Local Fourier Analysis","year":2021,"lang":"en","type":"article","venue":"SIAM Journal on Scientific Computing","topic":"Advanced Numerical Methods in Computational Mathematics","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Office of Science","keywords":"Multigrid method; Solver; Discretization; Mathematical optimization; Fourier transform; Mathematics; Fourier analysis; Fourier series; Algorithm; Computer science; Applied mathematics; Partial differential equation; Mathematical analysis","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":[0.001515604,0.0001694902,0.000316151,0.000337334,0.000410925,0.0004184393,0.0001277572,0.00004335359,0.00004472076],"category_scores_gemma":[0.0003692499,0.0001461094,0.00008812969,0.001908821,0.0001252239,0.0001600199,0.00006838148,0.0004029043,0.000002423043],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009351553,"about_ca_system_score_gemma":0.00004045396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":1.551e-7,"about_ca_topic_score_gemma":5.341004e-7,"domain_scores_codex":[0.998369,0.0002311574,0.0004061933,0.0003009962,0.0004235907,0.0002690851],"domain_scores_gemma":[0.9980558,0.001101811,0.0001444414,0.0002072756,0.0003192426,0.0001714484],"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":[0.000003036773,0.00001592047,0.00008367542,0.00002049649,0.0001522917,0.00003215492,0.0002287652,0.9009892,0.0001691831,0.0001555319,0.00001892031,0.09813082],"study_design_scores_gemma":[0.0002027335,0.00002183813,0.0001511912,0.00009796445,0.0001145297,0.0001902506,0.0002848759,0.99689,0.000702403,0.0008375811,0.0003277503,0.0001788441],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01163581,0.0002963049,0.986863,0.0000504793,0.0006837859,0.00004798079,0.000001244636,0.00009335715,0.0003279994],"genre_scores_gemma":[0.08669965,0.000008258702,0.9130799,0.00003117247,0.00009850778,5.109858e-7,0.000004271189,0.00002553274,0.00005221717],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.09795198,"threshold_uncertainty_score":0.5958169,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03491075878572143,"score_gpt":0.3338789458343272,"score_spread":0.2989681870486058,"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."}}