{"id":"W2278773995","doi":"10.1016/j.neunet.2016.04.001","title":"Synthesis of recurrent neural networks for dynamical system simulation","year":2016,"lang":"en","type":"article","venue":"Neural Networks","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":80,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Recurrent neural network; Computer science; Artificial neural network; Dynamical systems theory; Backpropagation; Dynamical system (definition); Feedforward neural network; Representation (politics); Relation (database); Artificial intelligence; Field (mathematics); Time delay neural network; Algorithm; Feed forward; Mathematics; Data mining; Control engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0004328254,0.0003426059,0.0005246681,0.0001008082,0.0002421748,0.0001157348,0.001139105,0.0002253608,0.000003715612],"category_scores_gemma":[0.000103551,0.0002232418,0.0003479704,0.0004796614,0.00008938609,0.0004210273,0.000432059,0.0002436262,0.000001694885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009142282,"about_ca_system_score_gemma":0.00001480531,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006354052,"about_ca_topic_score_gemma":0.000005614469,"domain_scores_codex":[0.9971153,0.0002428349,0.0007613518,0.0007168121,0.0003499612,0.000813743],"domain_scores_gemma":[0.9958307,0.002610219,0.0004179865,0.0007252858,0.0002078843,0.0002079367],"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":[0.00005157845,0.00002598323,0.0004034334,0.00003418962,0.00001746748,0.000006095202,0.000006877292,0.7224241,0.00002838192,0.002164328,0.0002233024,0.2746143],"study_design_scores_gemma":[0.0004389154,0.000182984,0.0006657281,0.0002673228,0.00002332675,0.00001734567,0.000004102154,0.9978223,0.00005509305,0.00007607843,0.0001526208,0.0002941377],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0446096,0.000358161,0.950525,0.0007711361,0.002764854,0.0005734112,0.000005124508,0.0003299144,0.00006279004],"genre_scores_gemma":[0.9961649,0.00002222826,0.002497733,0.00009758517,0.00109108,0.00005274279,0.00000375451,0.0000363094,0.00003372743],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9515553,"threshold_uncertainty_score":0.9103538,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01758279897425618,"score_gpt":0.2524493863507895,"score_spread":0.2348665873765333,"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."}}