{"id":"W2520963437","doi":"10.1109/hpcsim.2016.7568334","title":"Metis-CIC: A new mesh partitioning heuristic for parallel preconditioned iterative methods in CFD","year":2016,"lang":"en","type":"article","venue":"","topic":"VLSI and FPGA Design Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministry of Economy, Trade and Industry; National Natural Science Foundation of China","keywords":"Computer science; Metis; Parallel computing; Graph partition; Computational fluid dynamics; Iterative method; Rate of convergence; Mathematical optimization; Convergence (economics); Linear system; Heuristic; Algorithm; Graph; Theoretical computer science; Mathematics; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002547632,0.0001338786,0.0001964281,0.0001143018,0.00003272542,0.00003348165,0.00008150582,0.00007827293,0.0005419227],"category_scores_gemma":[0.0001022928,0.00009869292,0.00006030767,0.0001051973,0.00001626201,0.000245427,0.00001062117,0.00005643543,0.00002360059],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006369346,"about_ca_system_score_gemma":0.00001647953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001447115,"about_ca_topic_score_gemma":0.00003530268,"domain_scores_codex":[0.9992402,0.00005575865,0.000254668,0.0001671132,0.00005329431,0.0002289188],"domain_scores_gemma":[0.9993781,0.0003659434,0.00002441357,0.000132804,0.00002832973,0.00007038475],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001039281,0.0001215383,0.0007854202,0.0002588342,0.0003036299,0.00001761725,0.002719289,0.001518308,0.2196669,0.1242854,0.1593791,0.4908401],"study_design_scores_gemma":[0.003324606,0.0004056137,0.00240723,0.0005027115,0.00007166679,0.00002117727,0.0001305792,0.03883928,0.5359565,0.3945548,0.02262807,0.001157755],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0006374736,0.0001338646,0.9922513,0.0001664151,0.00008617321,0.0004107752,0.00001610218,0.00054468,0.005753203],"genre_scores_gemma":[0.2643895,0.00005916924,0.7320248,0.00008253202,0.00008747096,0.0005049938,0.00001363427,0.00003553605,0.002802422],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4896824,"threshold_uncertainty_score":0.5933673,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03214646894266986,"score_gpt":0.3334934494031846,"score_spread":0.3013469804605148,"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."}}