{"id":"W2962759047","doi":"10.1016/j.ces.2019.07.044","title":"Nonlinear model predictive control of a multiscale thin film deposition process using artificial neural networks","year":2019,"lang":"en","type":"article","venue":"Chemical Engineering Science","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":66,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial neural network; Nonlinear model; Model predictive control; Nonlinear system; Deposition (geology); Process (computing); Biological system; Materials science; Computer science; Artificial intelligence; Control theory (sociology); Control (management); Physics; Geology","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":[0.0001574819,0.0003420336,0.0004190656,0.0001626246,0.0003196436,0.0004712799,0.0004443319,0.000489087,0.0006877928],"category_scores_gemma":[0.000360306,0.0002502058,0.0003002898,0.0001614479,0.0003260762,0.0003730221,0.0004486831,0.0004590177,0.000063721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004689439,"about_ca_system_score_gemma":0.0004476319,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006217143,"about_ca_topic_score_gemma":0.006365392,"domain_scores_codex":[0.9999479,0.000007035091,0.00000282745,0.00001638584,0.00001861908,0.000007252838],"domain_scores_gemma":[0.9998943,0.00004594004,0.00002335765,0.000007895828,0.00002210966,0.000006481745],"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":[0.00004068845,0.00003158757,0.0003021635,0.00003119412,0.00001938014,0.00006603368,0.00001475044,0.9732528,0.01662152,0.001414884,0.0001424644,0.008062698],"study_design_scores_gemma":[0.000001033553,0.000004608773,0.00004806186,4.704451e-7,0.000001065402,0.000001330061,6.504666e-7,0.99933,0.0004961769,0.00009063407,0.00002511514,8.71266e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4999961,0.001001416,0.4859861,0.0005848308,0.0002914242,0.00006226551,0.00009415468,0.0003972998,0.01158652],"genre_scores_gemma":[0.9894893,0.0001213851,0.009027471,0.00001788577,0.00001265841,0.00002284216,0.00001752284,0.000007409824,0.001283611],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006217143,"threshold_uncertainty_score":0.01236194,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005490774416860594,"score_gpt":0.2111357200595602,"score_spread":0.2056449456426996,"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."}}