{"id":"W2166217501","doi":"10.1002/num.10032","title":"A domain decomposition preconditioner for hermite collocation problems","year":2003,"lang":"en","type":"article","venue":"Numerical Methods for Partial Differential Equations","topic":"Advanced Numerical Methods in Computational Mathematics","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Preconditioner; Mathematics; Domain decomposition methods; Piecewise; Discretization; Hermite polynomials; Collocation (remote sensing); Partial differential equation; Applied mathematics; Collocation method; Generalized minimal residual method; Multigrid method; Boundary (topology); Linear system; Finite element method; Mathematical analysis; Computer science; Differential equation","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":[0.000728202,0.0003972441,0.0006564638,0.0003081441,0.0003484916,0.0005874637,0.0005905365,0.0008827191,0.002225486],"category_scores_gemma":[0.001286397,0.0002648884,0.0004106968,0.0003627984,0.0006925096,0.0004942064,0.001051331,0.001478809,0.0008788707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002782548,"about_ca_system_score_gemma":0.0006691788,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009003498,"about_ca_topic_score_gemma":0.0009287215,"domain_scores_codex":[0.9993991,0.0001928633,0.00002608796,0.00006746875,0.0002613988,0.0000530635],"domain_scores_gemma":[0.999575,0.0001190784,0.00004416094,0.0001060205,0.0001212514,0.00003438736],"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":[0.0003004596,0.000196267,0.0008932535,0.0003364731,0.00006142366,0.0002688106,0.0002672343,0.4338974,0.1358778,0.1357037,0.009101017,0.2830961],"study_design_scores_gemma":[0.00003201762,0.00004850943,0.0001457206,0.00001053682,0.000006783604,0.00004833811,0.00001373497,0.9701986,0.01599379,0.007032033,0.006459218,0.00001074548],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006306396,0.00005099652,0.9922585,0.00007317319,0.00003317948,0.00001945264,0.00002075228,0.0001947667,0.001042791],"genre_scores_gemma":[0.1546195,0.000195542,0.8400596,0.0001141208,0.00007774564,0.0001588309,0.0001508319,0.0001372443,0.004486639],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002225486,"threshold_uncertainty_score":0.007444978,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05283478246619061,"score_gpt":0.4044903603816368,"score_spread":0.3516555779154462,"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."}}