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Optimal Control and Stochastic Parameter Estimation

Author

Listed:
  • Ngnepieba Pierre

    (1. Department of Mathematics, Florida A&M University, Tallahassee, Florida 32307, USA)

  • Hussaini M. Y.

    (2. School of Computational Science, Florida State University, Tallahassee, Florida 32306-4120, USA)

  • Debreu Laurent

    (2. School of Computational Science, Florida State University, Tallahassee, Florida 32306-4120, USA)

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.

Suggested Citation

  • Ngnepieba Pierre & Hussaini M. Y. & Debreu Laurent, 2006. "Optimal Control and Stochastic Parameter Estimation," Monte Carlo Methods and Applications, De Gruyter, vol. 12(5), pages 461-476, November.
    • Handle: RePEc:bpj:mcmeap:v:12:y:2006:i:5:p:461-476:n:6
    DOI: 10.1515/156939606779329062
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    Cited by:

    1. Jimenez Edwin & Lay Nathan & Hussaini M. Yousuff, 2010. "A systematic study of efficient sampling methods to quantify uncertainty in crack propagation and the Burgers equation," Monte Carlo Methods and Applications, De Gruyter, vol. 16(1), pages 69-93, January.

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