{"id":"W4390306070","doi":"10.48550/arxiv.2312.15177","title":"Stochastic Data-Driven Predictive Control with Equivalence to Stochastic MPC","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Model predictive control; Control theory (sociology); Equivalence (formal languages); Parametric statistics; Stochastic control; Noise (video); LTI system theory; Computer science; Sequence (biology); Control (management); Mathematics; Linear system; Mathematical optimization; Optimal control; Statistics; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009975662,0.0007001935,0.0008863875,0.0004166619,0.0002510347,0.0008263084,0.001338234,0.0005996544,0.001371208],"category_scores_gemma":[0.002855176,0.0003344536,0.000598903,0.0005804528,0.000745981,0.0006952192,0.001297682,0.001243071,0.0001806582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006464614,"about_ca_system_score_gemma":0.001198185,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003417016,"about_ca_topic_score_gemma":0.001720234,"domain_scores_codex":[0.9992037,0.0001606945,0.00003664552,0.0001500248,0.0003881753,0.00006062713],"domain_scores_gemma":[0.9990548,0.0004367772,0.0001401748,0.0001290542,0.0002078368,0.00003126175],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002527739,0.00002118529,0.0001313868,0.00006130668,0.00001546508,0.0000310049,0.00003056864,0.9462968,0.001258886,0.03196909,0.0003018804,0.01985707],"study_design_scores_gemma":[0.000002659279,0.00001149347,0.00002008872,0.000001900162,0.000001390355,0.000003509197,7.945348e-7,0.9965854,0.0002442552,0.002916187,0.0002104885,0.000001793095],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00349727,0.00007915413,0.9948778,0.00005960582,0.00002676903,0.00001737373,0.0000285393,0.0001358978,0.001277502],"genre_scores_gemma":[0.8651869,0.0002260896,0.1321376,0.0001230854,0.00009374072,0.0002019545,0.000177111,0.00008560697,0.001767928],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003417016,"threshold_uncertainty_score":0.006794274,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.066843586874238,"score_gpt":0.1936173588074761,"score_spread":0.1267737719332381,"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."}}