{"id":"W2006577846","doi":"10.1016/j.jprocont.2013.03.009","title":"Boundary model predictive control of thin film thickness modelled by the Kuramoto–Sivashinsky equation with input and state constraints","year":2013,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Fluid Dynamics and Thin Films","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Control theory (sociology); Model predictive control; Dissipative system; Boundary (topology); Quadratic equation; Mathematics; Boundary value problem; Representation (politics); Distributed parameter system; Modal; Controller (irrigation); Operator (biology); State (computer science); Optimal control; Applied mathematics; Mathematical analysis; Partial differential equation; Mathematical optimization; Computer science; Control (management); Algorithm; Physics; Geometry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003607437,0.0007148972,0.001025901,0.0002773696,0.0004463243,0.001329894,0.000856169,0.001020362,0.001531217],"category_scores_gemma":[0.001172705,0.0005456934,0.0004787842,0.0002781206,0.0008877541,0.000958564,0.001071453,0.001300429,0.0001802188],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007654564,"about_ca_system_score_gemma":0.0008447104,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01117048,"about_ca_topic_score_gemma":0.007932818,"domain_scores_codex":[0.9998785,0.00002394254,0.000004528387,0.00003418422,0.00003351049,0.00002525418],"domain_scores_gemma":[0.9995903,0.0002192695,0.00006653919,0.0000209843,0.00008446421,0.00001847866],"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":[0.00006775932,0.00002645698,0.0001751167,0.00005707948,0.00001250551,0.0000596678,0.00005108517,0.9821267,0.008617211,0.004358538,0.000243522,0.004204304],"study_design_scores_gemma":[0.000005251395,0.000008368712,0.00004795549,0.000002163155,0.00000222626,0.000001670171,0.000003238804,0.9987924,0.0006243333,0.000442912,0.00006708976,0.000002383439],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2919576,0.001027425,0.6893845,0.0007954403,0.0002603884,0.00006438012,0.0001696776,0.0004319316,0.01590869],"genre_scores_gemma":[0.9907565,0.0001794937,0.006041374,0.00003165814,0.00001582387,0.00004105119,0.00004815261,0.0000221254,0.002863872],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01117048,"threshold_uncertainty_score":0.0222109,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004693253940577221,"score_gpt":0.1858368241394917,"score_spread":0.1811435701989145,"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."}}