{"id":"W3135849906","doi":"10.15607/rss.2021.xvii.056","title":"Adaptive-Control-Oriented Meta-Learning for Nonlinear Systems","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Nuclear Safety and Security Commission; National Aeronautics and Space Administration; National Science Foundation","keywords":"Controller (irrigation); Control theory (sociology); Computer science; Nonlinear system; Adaptive control; Trajectory; Control engineering; A priori and a posteriori; Tracking error; Parametric statistics; Aerodynamics; Artificial neural network; Artificial intelligence; Control (management); Engineering; Mathematics","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000182251,0.0003254084,0.0007446946,0.00004867581,0.0001316106,0.0001630218,0.0001455934,0.0001382556,0.0009881579],"category_scores_gemma":[0.000006044485,0.0002646905,0.0007926461,0.00006918392,0.00002116234,0.00006246202,0.0001523967,0.0006737076,0.00001044924],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001945952,"about_ca_system_score_gemma":0.00009099465,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000250572,"about_ca_topic_score_gemma":0.000001890783,"domain_scores_codex":[0.998467,0.0001305849,0.0003790158,0.0005658322,0.0001643626,0.0002932193],"domain_scores_gemma":[0.9989749,0.00009903289,0.0002423328,0.0003124831,0.0002565448,0.0001147232],"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":[0.0001204522,0.0002168033,0.0002844856,0.0001225804,0.00806691,0.000002476953,0.0001235361,0.9508145,0.00009754736,0.0289716,0.004797304,0.006381739],"study_design_scores_gemma":[0.0006006047,0.00004053908,0.000005562222,0.00004143784,0.00125851,7.636934e-7,0.0005917826,0.9611877,0.0001638927,0.000177959,0.03559611,0.0003351379],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005633289,0.0006923255,0.9840834,0.0001757231,0.001872535,0.0009123667,0.00009907454,0.0001165588,0.006414698],"genre_scores_gemma":[0.9779811,0.00001014174,0.005596317,0.0000498132,0.001975596,0.0007040289,0.0006488613,0.00004944082,0.0129847],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9784871,"threshold_uncertainty_score":0.9999805,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04340348921532751,"score_gpt":0.2731088222007816,"score_spread":0.2297053329854541,"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."}}