Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Stochastic discontinuous Galerkin methods for robust deterministic control of convection-diffusion equations with uncertain coefficients
Date
2023-04-01
Author
Çiloğlu, Pelin
Yücel, Hamdullah
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
193
views
0
downloads
Cite This
We investigate a numerical behavior of robust deterministic optimal control problem subject to a convection-diffusion equation containing uncertain inputs. Stochastic Galerkin approach, turning the original optimization problem containing uncertainties into a large system of deterministic problems, is applied to discretize the stochastic domain, while a discontinuous Galerkin method is preferred for the spatial discretization due to its better convergence behavior for optimization problems governed by convection dominated PDEs. Error analysis is done for the state and adjoint variables in the energy norm, while the estimate of deterministic control is obtained in the L2-norm. Large matrix system emerging from the stochastic Galerkin method is addressed by the low-rank version of GMRES method, which reduces both the computational complexity and the memory requirements by employing Kronecker-product structure of the obtained linear system. Benchmark examples with and without control constraints are presented to illustrate the efficiency of the proposed methodology.
URI
https://hdl.handle.net/11511/102233
Journal
ADVANCES IN COMPUTATIONAL MATHEMATICS
DOI
https://doi.org/10.1007/s10444-023-10015-5
Collections
Graduate School of Applied Mathematics, Article
Suggestions
OpenMETU
Core
Dynamic programming for a Markov-switching jump-diffusion
Azevedo, N.; Pinheiro, D.; Weber, Gerhard Wilhelm (Elsevier BV, 2014-09-01)
We consider an optimal control problem with a deterministic finite horizon and state variable dynamics given by a Markov-switching jump-diffusion stochastic differential equation. Our main results extend the dynamic programming technique to this larger family of stochastic optimal control problems. More specifically, we provide a detailed proof of Bellman's optimality principle (or dynamic programming principle) and obtain the corresponding Hamilton-Jacobi-Belman equation, which turns out to be a partial in...
Stochastic modeling of biochemical systems with filtering and smoothing
Haksever, Merve; Uğur, Ömür; Department of Scientific Computing (2019)
Deterministic modeling approach is the traditional way of analyzing the dynamical behavior of a reaction network. However, this approach ignores the discrete and stochastic nature of biochemical processes. In this study, modeling approaches, stochastic simulation algorithms and their relationships to each other are investigated. Then, stochastic and deterministic modeling approaches are applied to biological systems, Lotka-Volterra prey-predator model, Michaelis-Menten enzyme kinetics and JACK-STAT signalin...
Local improvements to reduced-order approximations of optimal control problems governed by diffusion-convection-reaction equation
Akman, Tuğba (2015-07-01)
We consider the optimal control problem governed by diffusion-convection-reaction equation without control constraints. The proper orthogonal decomposition (POD) method is used to reduce the dimension of the problem. The POD method may lack accuracy if the POD basis depending on a set of parameters is used to approximate the problem depending on a different set of parameters. To increase the accuracy and the robustness of the basis, we compute five bases additional to the baseline POD in case of the perturb...
Stochastic Discontinuous Galerkin Methods with Low-Rank Solvers for Convection Diffusion Equations
Çiloğlu, Pelin; Yücel, Hamdullah (2021-09-06)
To simulate complex kinds of behavior in physical systems, one makes predictions and hypotheses about certain outputs of interest with the help of simulation of mathematical models. However, due to the lack of knowledge or inherent variability in the model parameters, such real-problems formulated by mathematical models generally come with uncertainty concerning computed quantities. Therefore, the idea of uncertainty quantification (UQ) has become a powerful tool for modeling physical phenomena in the last ...
Signaling Games for Log-Concave Distributions: Number of Bins and Properties of Equilibria
Kazikli, Ertan; Sarıtaş, Serkan; GEZİCİ, Sinan; Linder, Tamas; Yuksel, Serdar (2022-03-01)
We investigate the equilibrium behavior for the decentralized cheap talk problem for real random variables and quadratic cost criteria in which an encoder and a decoder have misaligned objective functions. In prior work, it has been shown that the number of bins in any equilibrium has to be countable, generalizing a classical result due to Crawford and Sobel who considered sources with density supported on [0, 1]. In this paper, we first refine this result in the context of log-concave sources. For sources ...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
P. Çiloğlu and H. Yücel, “Stochastic discontinuous Galerkin methods for robust deterministic control of convection-diffusion equations with uncertain coefficients,”
ADVANCES IN COMPUTATIONAL MATHEMATICS
, vol. 49, pp. 16–16, 2023, Accessed: 00, 2023. [Online]. Available: https://hdl.handle.net/11511/102233.