{"id":"W7132873076","doi":"","title":"Efficient Deep Learning Methods for Solving High-dimensional Partial Differential Equations for Applications in Option Pricing","year":2022,"lang":"","type":"dissertation","venue":"TSpace","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Curse of dimensionality; Benchmark (surveying); Artificial neural network; Deep learning; Reinforcement learning; Convergence (economics); Variety (cybernetics); Partial 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0005720621,0.0005032657,0.0006178953,0.0003181479,0.001796232,0.0001372208,0.0002152293,0.0002141055,0.0008138526],"category_scores_gemma":[0.00007337877,0.000575995,0.0004861186,0.0004461547,0.00003795727,0.00006385464,0.00008457295,0.0008368288,0.000005023531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002496109,"about_ca_system_score_gemma":0.0001915968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001788439,"about_ca_topic_score_gemma":0.00001858112,"domain_scores_codex":[0.9969901,0.0002822285,0.0008138167,0.0009489291,0.000295776,0.0006690993],"domain_scores_gemma":[0.9972332,0.001346513,0.0007625037,0.0002721321,0.0002339781,0.0001517131],"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":[0.0002917262,0.0004050522,0.00002025602,0.0001159334,0.00009818666,6.107927e-8,0.003757505,0.870173,0.01153457,0.04079085,0.00001578285,0.07279713],"study_design_scores_gemma":[0.001341465,0.0001589078,0.0001244206,0.00007595055,0.0003056735,2.60467e-7,0.006626251,0.984925,0.00261116,0.0007108686,0.002594618,0.000525404],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1357058,0.0002704635,0.8588706,0.0001204404,0.001427936,0.003484525,0.00002851979,0.00004499604,0.00004664749],"genre_scores_gemma":[0.9511296,0.00001205514,0.02745121,0.0000112969,0.001088813,0.008006202,0.008581134,0.0001091194,0.003610615],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8314194,"threshold_uncertainty_score":0.9996691,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02932951899914575,"score_gpt":0.3893735101790623,"score_spread":0.3600439911799165,"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."}}