Solving Optimal Control Problems Containing Uncertain Coefficients with Stochastic Discontinuous Galerkin Methods



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The present work introduces a method to solve constrained nonlinear optimal control problems using state-dependent coefficient factorization and Chebyshev polynomials. A recursive approximation technique known as approximating sequence of Riccati equations is used to replace the nonlinear problem by a sequence of linear-quadratic and time-varying approximating problems. The state variables are approximated and expanded in Chebyshev polynomials. Then, the control variables are written as a function of state ...
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Seymen, Zahire; Karasözen, Bülent; Department of Mathematics (2013)
Optimal control problems (OCPs) governed by convection dominated diffusion-convection-reaction equations arise in many science and engineering applications such as shape optimization of the technological devices, identification of parameters in environmental processes and flow control problems. A characteristic feature of convection dominated optimization problems is the presence of sharp layers. In this case, the Galerkin finite element method performs poorly and leads to oscillatory solutions. Hence, thes...
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One of the goals of Electroencephalography (EEG) is to correctly localize brain activities by the help of voltage measurements taken on scalp. However, due to computational difficulties of the problem and technological limitations, the accuracy level of the activity localization is not perfect and should be improved. To increase accuracy level of the solution, realistic, i.e. patient dependent, head models should be created. Such head models are created via assigning realistic conductivity values of head ti...
Citation Formats
P. Çiloğlu and H. Yücel, “Solving Optimal Control Problems Containing Uncertain Coefficients with Stochastic Discontinuous Galerkin Methods,” presented at the 17th Copper Mountain Conference on Iterative Methods, Colorado, Amerika Birleşik Devletleri, 2022, Accessed: 00, 2022. [Online]. Available: