{"id":"W2014339952","doi":"10.1090/s0025-5718-04-01718-1","title":"A locally conservative LDG method for the incompressible Navier-Stokes equations","year":2004,"lang":"en","type":"article","venue":"Mathematics of Computation","topic":"Advanced Numerical Methods in Computational Mathematics","field":"Engineering","cited_by":305,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Mathematisches Forschungsinstitut Oberwolfach; University of Minnesota; National Science Foundation","keywords":"Mathematics; Discretization; Navier–Stokes equations; Mathematical analysis; Uniqueness; Applied mathematics; Convergence (economics); Fixed-point iteration; Discontinuous Galerkin method; Incompressible flow; Divergence (linguistics); Iterative method; Compressibility; Finite element method; Fixed point; Mathematical optimization; Flow (mathematics); 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":[0.000538961,0.0004797198,0.0006296503,0.0006580402,0.0003867436,0.0008390545,0.001091975,0.0008716995,0.00246208],"category_scores_gemma":[0.000667097,0.0002069646,0.0005315535,0.000338768,0.0009018007,0.0007829749,0.0009978603,0.0008900372,0.000819125],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005321405,"about_ca_system_score_gemma":0.000669066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001443056,"about_ca_topic_score_gemma":0.001298107,"domain_scores_codex":[0.9997073,0.00009250206,0.00000984737,0.00004105271,0.0001300443,0.00001922411],"domain_scores_gemma":[0.9997908,0.00008627566,0.00002061914,0.00003056071,0.00004752759,0.00002421377],"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":[0.0001504782,0.0001187716,0.002275867,0.0006257053,0.00005599798,0.0004495061,0.0005238978,0.4952988,0.05244137,0.1291848,0.003900153,0.3149746],"study_design_scores_gemma":[0.00001516836,0.00004070339,0.0001667647,0.00002988327,0.000009204499,0.00007870702,0.00003094162,0.9786882,0.003667781,0.00780241,0.009452322,0.00001786966],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004975072,0.0002622664,0.992968,0.00008232715,0.00004938957,0.0000213377,0.00002164761,0.0001839141,0.001436001],"genre_scores_gemma":[0.2318147,0.0006720051,0.7555785,0.0001940194,0.00008318911,0.0002296513,0.0001585437,0.0002706514,0.01099883],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00246208,"threshold_uncertainty_score":0.008236527,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04671312687074364,"score_gpt":0.3610357050598986,"score_spread":0.3143225781891549,"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."}}