{"id":"W3023870669","doi":"10.1109/access.2020.2991966","title":"Using Neural Networks for Fast Numerical Integration and Optimization","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Numerical integration; Bounded function; Function (biology); Dimension (graph theory); Artificial neural network; Computer science; Domain (mathematical analysis); Set (abstract data type); Polyhedron; Algorithm; Applied mathematics; Mathematical optimization; Mathematics; Artificial intelligence; Mathematical analysis; Pure mathematics; Combinatorics","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":[],"consensus_categories":[],"category_scores_codex":[0.00001901724,0.00007936704,0.00009322762,0.00001239029,0.00009198474,0.0001255744,0.00006997996,0.00002482579,0.00005970025],"category_scores_gemma":[0.000001934103,0.00007139376,0.00003522333,0.0001044081,0.00001444203,0.0003067825,0.00001931653,0.0000816507,3.147512e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004440286,"about_ca_system_score_gemma":0.000005640242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001906796,"about_ca_topic_score_gemma":1.989273e-7,"domain_scores_codex":[0.9995618,0.00001627091,0.0001118607,0.0001585564,0.00004271889,0.0001088242],"domain_scores_gemma":[0.9997747,0.00001835171,0.00005519023,0.00004474755,0.00003418115,0.00007285969],"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":[0.00002590928,0.00000798311,0.0005840866,0.000002408093,0.000006407102,8.14151e-8,0.0000370357,0.9684189,0.00008958855,0.0002780014,0.0004593698,0.03009024],"study_design_scores_gemma":[0.0002004974,0.00001912142,0.00002499639,0.000003831264,0.00001332724,4.430044e-7,0.00002614416,0.9990515,0.0003976758,0.00008362269,0.0001011869,0.00007769479],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04312745,0.00001436476,0.9558994,0.0004005828,0.0002635548,0.0001650093,0.000004153089,0.0000227891,0.0001027583],"genre_scores_gemma":[0.9952801,0.00000221823,0.003210588,0.0003427336,0.001099654,0.00001365809,0.00002801478,0.00001180465,0.00001120915],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9526888,"threshold_uncertainty_score":0.2911353,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07365442554767984,"score_gpt":0.3271018795100532,"score_spread":0.2534474539623733,"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."}}