{"id":"W2321070695","doi":"10.2514/6.2013-383","title":"Time-Accurate Flow Simulations Using an Efficient Newton-Krylov-Schur Approach with High-Order Temporal and Spatial Discretization","year":2013,"lang":"en","type":"article","venue":"51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition","topic":"Computational Fluid Dynamics and Aerodynamics","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Discretization; Computer science; Flow (mathematics); Applied mathematics; Computational science; Order (exchange); Newton's method; Mathematical optimization; Algorithm; Parallel computing; Mathematics; Mathematical analysis; Physics; Geometry; Nonlinear system","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0003557235,0.000381351,0.0002740301,0.0001434653,0.001809487,0.0008138415,0.0002339336,0.0001119791,0.00001048522],"category_scores_gemma":[0.00004644913,0.0002899177,0.00003868113,0.0008055604,0.0004140465,0.0007514937,0.0001791514,0.0002253942,0.000005548583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001087648,"about_ca_system_score_gemma":0.00007754604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001100199,"about_ca_topic_score_gemma":0.000450451,"domain_scores_codex":[0.9979745,0.00009022693,0.0003182378,0.0005591959,0.0004781944,0.0005796835],"domain_scores_gemma":[0.9990759,0.0001742659,0.0001513317,0.0002325833,0.0001434322,0.0002224405],"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":[0.00000930624,0.00003682531,0.002101618,0.00001686096,0.00001895844,3.975194e-7,0.0005161706,0.9734152,0.02233718,0.0009741014,0.00007691677,0.0004964966],"study_design_scores_gemma":[0.0004651806,0.0002560203,0.00174355,0.0001199118,0.00004016671,0.00001982418,0.001077731,0.9952943,0.0002373024,0.0003075676,0.00001730606,0.0004211592],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6879742,0.00008163373,0.3097811,0.001265444,0.0001564136,0.0004482247,0.0000235447,0.0001690994,0.0001003811],"genre_scores_gemma":[0.9458663,0.0000215702,0.05364697,0.00004632116,0.0001526924,0.00001696444,0.0001314964,0.00005174887,0.00006588939],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2578922,"threshold_uncertainty_score":0.9999553,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01098806772887973,"score_gpt":0.2246050965101639,"score_spread":0.2136170287812841,"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."}}