{"id":"W2898545526","doi":"10.1007/s00245-021-09792-6","title":"A Stochastic Maximum Principle for Control Problems Constrained by the Stochastic Navier–Stokes Equations","year":2021,"lang":"en","type":"preprint","venue":"Applied Mathematics & Optimization","topic":"Stochastic processes and financial applications","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ducks Unlimited Canada","funders":"International Max Planck Research School for Environmental, Cellular and Molecular Microbiology; International Max Planck Research School for Advanced Methods in Process and Systems Engineering; Friedrich-Schiller-Universität Jena","keywords":"Mathematics; Stochastic control; Optimal control; Maximum principle; Control (management); Applied mathematics; Navier–Stokes equations; Adjoint equation; Stochastic differential equation; Mathematical optimization; Mathematical analysis; Partial differential equation; Computer science; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006931791,0.0005284227,0.000988903,0.0001648283,0.0005301395,0.0004172795,0.0006746748,0.0004342522,0.00009364734],"category_scores_gemma":[0.0007112725,0.0005334428,0.0002565291,0.0003900549,0.0001990739,0.00009705225,0.0002635607,0.0004638693,0.0000505588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001861239,"about_ca_system_score_gemma":0.0002355579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002925929,"about_ca_topic_score_gemma":0.00001163799,"domain_scores_codex":[0.9968807,0.000006340078,0.001535124,0.0009091375,0.0001467862,0.0005219054],"domain_scores_gemma":[0.9962298,0.000744921,0.001652957,0.0009398405,0.000312555,0.0001199835],"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":[0.00000764817,0.0002031402,2.115812e-7,0.0002869945,0.0001017788,7.64304e-8,0.0006668867,0.5409751,0.00001618315,0.4574186,0.00008701562,0.0002364265],"study_design_scores_gemma":[0.0007871818,0.00002423187,9.474084e-7,0.00009724538,0.0001333623,0.000002978178,0.0002170507,0.6311242,0.000004839218,0.3670653,0.000137054,0.0004056513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00006880299,0.001183131,0.9885615,0.0009081133,0.0003176859,0.005517496,0.002035812,0.0001438878,0.001263565],"genre_scores_gemma":[0.6489197,0.00005661784,0.3295274,0.0004556835,0.0003197864,0.01718383,0.003077495,0.0002320153,0.0002274872],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6590341,"threshold_uncertainty_score":0.9997117,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02696804636024109,"score_gpt":0.2395911308703906,"score_spread":0.2126230845101496,"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."}}