{"id":"W2911774660","doi":"10.1016/j.jcp.2019.01.003","title":"Optimal Runge–Kutta schemes for pseudo time-stepping with high-order unstructured methods","year":2019,"lang":"en","type":"article","venue":"Journal of Computational Physics","topic":"Computational Fluid Dynamics and Aerodynamics","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Engineering and Physical Sciences Research Council; Natural Sciences and Engineering Research Council of Canada; Western Canada Research Grid; Compute Canada","keywords":"Speedup; Applied mathematics; Discontinuous Galerkin method; Runge–Kutta methods; Turbulence; Compressibility; Large eddy simulation; Airfoil; Advection; Mathematics; Context (archaeology); Computer science; Mathematical analysis; Numerical analysis; Physics; Mechanics; Finite element method; Parallel computing","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":[0.0007441903,0.0004626989,0.0004492242,0.0004057679,0.0003578204,0.0005787858,0.0007220092,0.0005816622,0.001442711],"category_scores_gemma":[0.002759362,0.0003223741,0.0004781646,0.0002873399,0.0007513017,0.0006388035,0.0007417101,0.0008520015,0.0005410634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004677534,"about_ca_system_score_gemma":0.001019262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00118757,"about_ca_topic_score_gemma":0.001689038,"domain_scores_codex":[0.9994967,0.0001686873,0.00003226834,0.00004531351,0.0002133907,0.00004360709],"domain_scores_gemma":[0.9992788,0.0002663224,0.0001034999,0.0001551774,0.000159904,0.00003623651],"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":[0.0002296068,0.0001211604,0.002006598,0.0001956728,0.0000365887,0.00007501988,0.0001597452,0.8804303,0.02834848,0.04620308,0.001047163,0.04114661],"study_design_scores_gemma":[0.00001986866,0.00004197524,0.0001360557,0.0000073352,0.000004390512,0.00001457572,0.000009106041,0.9898577,0.005740204,0.002820536,0.001340899,0.000007419571],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07427189,0.0002459617,0.9204513,0.00007365146,0.00006432744,0.00008117353,0.00008319524,0.0004845714,0.004243935],"genre_scores_gemma":[0.3408138,0.0001256676,0.6568197,0.00003460536,0.00001965989,0.0002090903,0.0001767313,0.0002252667,0.00157548],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001442711,"threshold_uncertainty_score":0.004826307,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004557636061620244,"score_gpt":0.2417789302943704,"score_spread":0.2372212942327502,"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."}}