{"id":"W3046589003","doi":"10.1007/s11009-021-09871-9","title":"Solving Elliptic Equations with Brownian Motion: Bias Reduction and Temporal Difference Learning","year":2021,"lang":"en","type":"article","venue":"Methodology And Computing In Applied Probability","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Discretization; Applied mathematics; Stochastic differential equation; Brownian motion; Markov process; Partial differential equation; Feynman diagram; Mathematical optimization; Mathematical analysis; Statistics","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":[0.002225013,0.0006492315,0.000967461,0.0006200313,0.0003939093,0.001146177,0.001646888,0.001751878,0.001420818],"category_scores_gemma":[0.009853479,0.0006968593,0.0008018971,0.0006486708,0.001491897,0.002235024,0.002493517,0.002370571,0.0002469259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007348565,"about_ca_system_score_gemma":0.00139727,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003092213,"about_ca_topic_score_gemma":0.002616318,"domain_scores_codex":[0.9994381,0.0002525003,0.00003163829,0.00009704156,0.0001418687,0.00003877129],"domain_scores_gemma":[0.9971393,0.002056176,0.0002073623,0.0001772606,0.0003025238,0.0001173824],"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":[0.0001384868,0.0001218251,0.001100832,0.0002487143,0.0001162032,0.00007679992,0.0001506822,0.6507777,0.005625794,0.2724597,0.001858227,0.06732508],"study_design_scores_gemma":[0.000006598812,0.00000717096,0.00004816686,0.000004074842,0.000003612104,0.000006217808,0.000003102204,0.9716853,0.0004034085,0.02756041,0.0002678703,0.00000405862],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009616411,0.0003161497,0.9886637,0.0004151954,0.00006779146,0.00001374847,0.00001532645,0.00006074707,0.0008309652],"genre_scores_gemma":[0.3813964,0.001227297,0.6051568,0.0004614418,0.0005035372,0.0002521883,0.0001943326,0.0003146095,0.01049323],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003092213,"threshold_uncertainty_score":0.01176715,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.117952890770332,"score_gpt":0.3010742739524253,"score_spread":0.1831213831820933,"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."}}