{"id":"W3209580039","doi":"10.1002/num.23065","title":"A posteriori error analysis for a space‐time parallel discretization of parabolic partial differential equations","year":2023,"lang":"en","type":"article","venue":"Numerical Methods for Partial Differential Equations","topic":"Advanced Numerical Methods in Computational Mathematics","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"National Science Foundation of Sri Lanka; Natural Sciences and Engineering Research Council of Canada","keywords":"Domain decomposition methods; Discretization; Partial differential equation; Mathematics; Solver; Estimator; Applied mathematics; Finite element method; Mortar methods; A priori and a posteriori; Numerical partial differential equations; Algorithm; Method of lines; Space (punctuation); Mathematical optimization; Differential equation; Mathematical analysis; Computer science; Ordinary differential equation; Differential algebraic equation","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005486261,0.0005344934,0.001300256,0.0006575155,0.0003482823,0.00009505876,0.0004117681,0.0002622611,0.0001979139],"category_scores_gemma":[0.004276996,0.0005294246,0.0009925434,0.002669909,0.0001790757,0.0002350884,0.0001126736,0.0002270219,0.00004153564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001039329,"about_ca_system_score_gemma":0.00006431574,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009634413,"about_ca_topic_score_gemma":0.000002438186,"domain_scores_codex":[0.9960147,0.0005446777,0.001483353,0.0006434154,0.0004973512,0.0008164769],"domain_scores_gemma":[0.9903284,0.008029034,0.0004279806,0.0005545038,0.000352914,0.0003071838],"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":[0.0003242996,0.0004144146,0.00004591971,0.0003257214,0.001878869,5.493744e-7,0.0007780027,0.7776107,0.05723024,0.1266149,0.0001168301,0.03465965],"study_design_scores_gemma":[0.001032991,0.0002479433,0.000621236,0.00002865909,0.001566819,5.21426e-7,0.00004282115,0.9571693,0.005336094,0.03289712,0.0005240011,0.0005325173],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003126717,0.00005215947,0.992381,0.0002427996,0.000977449,0.00181398,0.0006711885,0.0006955888,0.00003909362],"genre_scores_gemma":[0.4000047,0.000009957694,0.5965822,0.00001516014,0.000369913,0.001676496,0.001087133,0.0001186001,0.0001357502],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.396878,"threshold_uncertainty_score":0.9997157,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07740712232355203,"score_gpt":0.4077862917820096,"score_spread":0.3303791694584576,"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."}}