{"id":"W2016705856","doi":"10.5402/2012/246491","title":"Parallel Adaptive Mesh Refinement Combined with Additive Multigrid for the Efficient Solution of the Poisson Equation","year":2012,"lang":"en","type":"article","venue":"ISRN Applied Mathematics","topic":"Computational Fluid Dynamics and Aerodynamics","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada; University of Waterloo; Waterloo CFD Engineering Consulting","funders":"","keywords":"Multigrid method; Parallelizable manifold; Adaptive mesh refinement; Discretization; Preconditioner; Solver; Computer science; Grid; Applied mathematics; Cartesian coordinate system; Parallel computing; Computational science; Mathematical optimization; Poisson's equation; Algorithm; Mathematics; Partial differential equation; Iterative method; Mathematical analysis; Geometry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007137157,0.0003409565,0.0005490978,0.0005114102,0.0003308585,0.0004128918,0.000757289,0.0003797679,0.001265179],"category_scores_gemma":[0.001439168,0.0002214656,0.0006673803,0.0009862544,0.000442404,0.0007573329,0.0008713416,0.0008360178,0.000350199],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002948464,"about_ca_system_score_gemma":0.0008945393,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002910658,"about_ca_topic_score_gemma":0.002772331,"domain_scores_codex":[0.9993827,0.0001670498,0.00002387864,0.00004380927,0.0003383351,0.00004423354],"domain_scores_gemma":[0.9995973,0.0001927244,0.00002874105,0.00008347916,0.00007849549,0.00001932076],"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":[0.0002423986,0.0001219382,0.002514038,0.0003755059,0.0001027025,0.0004311629,0.0004280771,0.4698853,0.1065341,0.07733186,0.004242865,0.3377901],"study_design_scores_gemma":[0.00002094815,0.00002916651,0.000220234,0.000005098529,0.000010482,0.0000677746,0.000009450494,0.9814612,0.01110901,0.003662246,0.003395122,0.000009350383],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01788784,0.0002113294,0.9786618,0.00009475397,0.00004721116,0.00003057565,0.00002436792,0.0006094637,0.002432513],"genre_scores_gemma":[0.2418569,0.000233613,0.7555168,0.00004631157,0.00002891699,0.0000941607,0.0001048133,0.0001477946,0.001970564],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002910658,"threshold_uncertainty_score":0.005787492,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01592515572622432,"score_gpt":0.2105741858605313,"score_spread":0.1946490301343069,"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."}}