{"id":"W2507205846","doi":"10.1615/int.j.uncertaintyquantification.2016015843","title":"A PRIORI ERROR ANALYSIS OF STOCHASTIC GALERKIN PROJECTION SCHEMES FOR RANDOMLY PARAMETRIZED ORDINARY DIFFERENTIAL EQUATIONS","year":2016,"lang":"en","type":"article","venue":"International Journal for Uncertainty Quantification","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Mathematics; Applied mathematics; A priori and a posteriori; Galerkin method; Discretization; Ordinary differential equation; Nonlinear system; Stochastic differential equation; Projection (relational algebra); Differential equation; Mathematical analysis; Algorithm","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":[0.005009649,0.001192488,0.001126008,0.0009483962,0.0004797225,0.00133325,0.001022574,0.001474995,0.0008241039],"category_scores_gemma":[0.01192236,0.0005503575,0.000808444,0.0004700618,0.002560239,0.001374523,0.002472311,0.00176712,0.0001259819],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001129837,"about_ca_system_score_gemma":0.001664295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003010931,"about_ca_topic_score_gemma":0.001172509,"domain_scores_codex":[0.998528,0.0006911308,0.00007498882,0.000154497,0.0004695543,0.00008172426],"domain_scores_gemma":[0.9914773,0.005976231,0.000970348,0.0003664326,0.0009956154,0.0002140078],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0000597272,0.00002527121,0.0005388218,0.0001144964,0.00003202905,0.00005245263,0.0001002779,0.8807316,0.003192723,0.1097848,0.0002649379,0.005102979],"study_design_scores_gemma":[0.000001422881,0.000007695234,0.00004621343,0.000005513399,0.000001448144,0.000003985213,0.000003162645,0.9943955,0.0003251603,0.005111616,0.00009417412,0.000004137312],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02944475,0.0004342478,0.9681334,0.0003400231,0.00003622193,0.00003397369,0.00004510094,0.00007289658,0.001459291],"genre_scores_gemma":[0.8739293,0.0009618802,0.1202814,0.0001164122,0.0000856516,0.0002509664,0.000228531,0.0001349727,0.004010874],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005009649,"threshold_uncertainty_score":0.02649391,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1910434288458941,"score_gpt":0.4363292191118509,"score_spread":0.2452857902659568,"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."}}