{"id":"W2083836337","doi":"10.1016/s0096-3003(01)00098-4","title":"Highly efficient parallel algorithm for finite difference solution to Navier–Stoke's equation on a hypercube","year":2002,"lang":"en","type":"article","venue":"Applied Mathematics and Computation","topic":"Matrix Theory and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"University of Waterloo; Department of Science and Technology, Ministry of Science and Technology, India","keywords":"Tridiagonal matrix; Tridiagonal matrix algorithm; Hypercube; Discretization; Generalization; Computation; Mathematics; Matrix (chemical analysis); Algorithm; Band matrix; Finite difference method; Reduction (mathematics); Finite difference; Applied mathematics; Parallel computing; Computer science; Mathematical analysis; Discrete mathematics; Symmetric matrix; Geometry; Square matrix; Physics; Eigenvalues and eigenvectors","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":[0.0005000617,0.0005464309,0.0006699408,0.0004118948,0.0009218336,0.0006687746,0.001069654,0.0005344119,0.005527264],"category_scores_gemma":[0.001457466,0.000293763,0.0003418944,0.0008462092,0.0004213025,0.0009199875,0.001026008,0.0008061858,0.00112225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006719464,"about_ca_system_score_gemma":0.001207213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003344544,"about_ca_topic_score_gemma":0.004412061,"domain_scores_codex":[0.9997292,0.00007010218,0.0000158643,0.00002766337,0.0001202772,0.00003687202],"domain_scores_gemma":[0.9994739,0.0001996319,0.00002785286,0.0000798464,0.0001795705,0.00003919796],"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":[0.001086398,0.000270472,0.001750913,0.0004016906,0.0001215932,0.0004969853,0.0004591055,0.5030379,0.03406536,0.07343278,0.01901739,0.3658594],"study_design_scores_gemma":[0.0001249331,0.00006712741,0.0002046857,0.000008318345,0.00001221646,0.00004851546,0.00004450242,0.9692694,0.007274225,0.01810284,0.004831256,0.00001196335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08849948,0.000486953,0.8915796,0.0007015946,0.0003190364,0.0001657628,0.0002126469,0.001802898,0.01623193],"genre_scores_gemma":[0.385267,0.0002718226,0.6021644,0.0001359715,0.00009842552,0.0004410138,0.0004375248,0.000259526,0.01092427],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005527264,"threshold_uncertainty_score":0.01849049,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03372380291306331,"score_gpt":0.2482052198847875,"score_spread":0.2144814169717242,"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."}}