{"id":"W3093624614","doi":"10.1137/20m1375103","title":"AIR Algebraic Multigrid for a Space-Time Hybridizable Discontinuous Galerkin Discretization of Advection(-Diffusion)","year":2021,"lang":"en","type":"preprint","venue":"SIAM Journal on Scientific Computing","topic":"Advanced Numerical Methods in Computational Mathematics","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Lawrence Livermore National Laboratory; University of Waterloo; Los Alamos National Laboratory; Compute Canada","keywords":"Discontinuous Galerkin method; Preconditioner; Advection; Discretization; Multigrid method; Applied mathematics; Mathematics; Convection–diffusion equation; Robustness (evolution); Mathematical analysis; Physics; Finite element method; Linear system; Partial differential equation","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003865787,0.0002617907,0.000280551,0.0002017049,0.0002099225,0.0004408044,0.0004333887,0.0002839797,0.001593776],"category_scores_gemma":[0.000813969,0.0001044186,0.0003990322,0.0001751033,0.0005932972,0.0003677482,0.0008620084,0.0007948322,0.0003927952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002778253,"about_ca_system_score_gemma":0.0002907573,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001400112,"about_ca_topic_score_gemma":0.00140516,"domain_scores_codex":[0.9997651,0.00007203495,0.00001017744,0.00002562222,0.0001105296,0.00001664271],"domain_scores_gemma":[0.9997392,0.0001094053,0.000032126,0.00005505557,0.00004881664,0.00001526631],"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":[0.0001702713,0.00006633114,0.001265868,0.000337692,0.00004298992,0.0001655374,0.0003176634,0.5491269,0.0494984,0.2890549,0.002427018,0.1075264],"study_design_scores_gemma":[0.00001290655,0.00003933564,0.0001818173,0.00001084729,0.000005898194,0.00004685321,0.0000162809,0.973861,0.005844812,0.009739357,0.01023417,0.000006639073],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01989796,0.0001812565,0.9745252,0.0001115492,0.00005536828,0.00003609073,0.00005244439,0.0003504537,0.004789718],"genre_scores_gemma":[0.4428149,0.0002176839,0.551147,0.00007581371,0.00003912945,0.0001140802,0.0001545084,0.0001386244,0.005298238],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001593776,"threshold_uncertainty_score":0.005331695,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01865777670806878,"score_gpt":0.2902435465917274,"score_spread":0.2715857698836586,"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."}}