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Optimal Control and Stochastic Parameter Estimation

Pierre Désiré Ngnepieba 1 M.Yussuf Hussaini 2 Laurent Debreu 3, 2, 4 
3 MOISE - Modelling, Observations, Identification for Environmental Sciences
Inria Grenoble - Rhône-Alpes, UJF - Université Joseph Fourier - Grenoble 1, INPG - Institut National Polytechnique de Grenoble , CNRS - Centre National de la Recherche Scientifique : UMR5227
Abstract : An efficient sampling method is proposed to solve the stochastic optimal control problem in the context of data assimilation for the estimation of a random parameter. It is based on Bayesian inference and the Markov Chain Monte Carlo technique, which exploits the relation between the inverse Hessian of the cost function and the error covariance matrix to accelerate convergence of the sampling method. The efficiency and accuracy of the method is demonstrated in the case of the optimal control problem governed by the nonlinear Burgers equation with a viscosity parameter that is a random field.
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https://hal.inria.fr/inria-00592665
Contributor : Laurent Debreu Connect in order to contact the contributor
Submitted on : Friday, May 13, 2011 - 9:33:35 AM
Last modification on : Saturday, November 19, 2022 - 3:58:49 AM

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Pierre Désiré Ngnepieba, M.Yussuf Hussaini, Laurent Debreu. Optimal Control and Stochastic Parameter Estimation. Monte Carlo Methods and Applications, 2006, 12 (5/6), pp.461-476. ⟨10.1515/156939606779329062⟩. ⟨inria-00592665⟩

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