{"id":"W4286904785","doi":"10.48550/arxiv.2110.07010","title":"Distributed and Localized Model Predictive Control. Part I: Synthesis and Implementation","year":2021,"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":"Division of Electrical, Communications and Cyber Systems; Natural Sciences and Engineering Research Council of Canada; California Institute of Technology; U.S. Department of Energy; National Science Foundation","keywords":"Scalability; Model predictive control; Computation; Computer science; Distributed computing; Stability (learning theory); Distributed element model; Scale (ratio); Control (management); State (computer science); Exponential stability; Control theory (sociology); Mathematical optimization; Algorithm; Mathematics; Engineering; Artificial intelligence; Nonlinear system","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00008846293,0.0002496742,0.0003720481,0.00008503814,0.00007126308,0.00005728518,0.00009605316,0.0001976506,0.00001333187],"category_scores_gemma":[0.00003346525,0.0003172533,0.00005627247,0.0001215351,0.00005216934,0.0002352999,0.000137592,0.0002005121,8.124718e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001964184,"about_ca_system_score_gemma":0.00003710996,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003558615,"about_ca_topic_score_gemma":0.0000532407,"domain_scores_codex":[0.9990002,0.00007241563,0.0001961122,0.0004777951,0.00005043035,0.0002030344],"domain_scores_gemma":[0.9993169,0.000101313,0.0001008292,0.0002641827,0.0001068653,0.0001098505],"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":[0.00004268681,0.000007318011,0.001198338,0.0001304065,0.0002203525,0.00002132873,0.00007363145,0.997092,0.00007214263,0.0008725725,0.00003351901,0.0002356916],"study_design_scores_gemma":[0.001123938,0.00001041946,0.0003958196,0.00009226933,0.0002975177,0.000002056297,0.0003902543,0.9962939,0.00009290527,0.001004546,0.00003177539,0.0002645762],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1385261,0.0002811481,0.8598783,0.00001321405,0.0001072608,0.0004268097,0.00043872,0.0002058697,0.000122563],"genre_scores_gemma":[0.9983994,0.0008097486,0.0004920429,0.00001008934,0.00003185764,0.00001273658,0.0001854332,0.00003209535,0.00002655107],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8598734,"threshold_uncertainty_score":0.9999279,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02214234357476795,"score_gpt":0.1732442189649759,"score_spread":0.1511018753902079,"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."}}