{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009664163,0.0008958209,0.0006681134,0.0005451135,0.00034781,0.0009172863,0.0008644147,0.0009161168,0.002153338],"category_scores_gemma":[0.002696109,0.0004815499,0.0005027451,0.0007939613,0.000670485,0.001237476,0.001169887,0.001503916,0.000795573],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008364343,"about_ca_system_score_gemma":0.0007606311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00513396,"about_ca_topic_score_gemma":0.005733956,"domain_scores_codex":[0.9995894,0.0001145791,0.00002302458,0.0000554239,0.0001828379,0.00003485694],"domain_scores_gemma":[0.999405,0.0003454516,0.00005675386,0.0000741244,0.0001033408,0.00001543298],"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":[0.00006967122,0.00004011908,0.0004046941,0.0001272208,0.00005816693,0.00005426937,0.00005498617,0.8286946,0.006888275,0.03412102,0.001390426,0.1280966],"study_design_scores_gemma":[0.000002465413,0.000005971669,0.00002255045,0.000003966337,0.000002157019,0.000005527075,0.00000162308,0.9953165,0.0007618615,0.003108255,0.0007664493,0.000002637078],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005288281,0.000443422,0.9907417,0.0001572923,0.00004946171,0.00001879571,0.00002151754,0.0005399417,0.002739589],"genre_scores_gemma":[0.2471957,0.0008564753,0.7453592,0.0001928175,0.00009513462,0.0001872314,0.0001717844,0.0003379367,0.005603668],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00513396,"threshold_uncertainty_score":0.01020813,"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."}}