{"id":"W2804733453","doi":"10.1016/j.jcp.2018.05.020","title":"Optimization of high-order diagonally-implicit Runge–Kutta methods","year":2018,"lang":"en","type":"article","venue":"Journal of Computational Physics","topic":"Numerical methods for differential equations","field":"Mathematics","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Mathematics; Runge–Kutta methods; Airfoil; Applied mathematics; Order of accuracy; Approximation error; Stability (learning theory); NACA airfoil; Numerical analysis; Norm (philosophy); Mathematical analysis; Numerical stability; Turbulence; Reynolds number; Computer science; Physics; Mechanics","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":[0.001969521,0.001081416,0.001593595,0.0007984434,0.0009074117,0.001468352,0.001810495,0.00189471,0.005276081],"category_scores_gemma":[0.006028971,0.0007522688,0.0008389533,0.0006238127,0.001414512,0.00136388,0.001679671,0.001904154,0.001267653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009921442,"about_ca_system_score_gemma":0.002610111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004436888,"about_ca_topic_score_gemma":0.006330691,"domain_scores_codex":[0.9990032,0.0004798787,0.00005104263,0.0001029361,0.0002667519,0.0000960609],"domain_scores_gemma":[0.9976327,0.001265146,0.0001585316,0.0002450409,0.0005717349,0.0001268902],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000197157,0.00018605,0.000491094,0.0002246827,0.00004334267,0.00006232547,0.0001207673,0.9209749,0.004359665,0.02880549,0.00217086,0.04236386],"study_design_scores_gemma":[0.00001066513,0.00001736139,0.00004630759,0.000005879725,0.000002967845,0.00000422184,0.000005435907,0.9968237,0.0004743539,0.002104985,0.0004986956,0.000005457651],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02767122,0.000466698,0.9631291,0.0002301151,0.0001860636,0.00009892276,0.00009265915,0.0004532045,0.007672057],"genre_scores_gemma":[0.4699938,0.0004254989,0.5146331,0.0001843935,0.0001537803,0.0005375041,0.0003628492,0.00103446,0.01267453],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005276081,"threshold_uncertainty_score":0.01765025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06310288411325624,"score_gpt":0.4179369626204934,"score_spread":0.3548340785072371,"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."}}