Optimal design of acoustic metamaterial cloaks under uncertainty
In this work, we consider the problem of optimal design of an acoustic cloak under
uncertainty and develop scalable approximation and optimization methods to solve this …
uncertainty and develop scalable approximation and optimization methods to solve this …
Risk-averse PDE-constrained optimization using the conditional value-at-risk
DP Kouri, TM Surowiec - SIAM Journal on Optimization, 2016 - SIAM
Uncertainty is inevitable when solving science and engineering application problems. In the
face of uncertainty, it is essential to determine robust and risk-averse solutions. In this work …
face of uncertainty, it is essential to determine robust and risk-averse solutions. In this work …
A quasi-Monte Carlo method for optimal control under uncertainty
We study an optimal control problem under uncertainty, where the target function is the
solution of an elliptic partial differential equation with random coefficients, steered by a …
solution of an elliptic partial differential equation with random coefficients, steered by a …
Reduced basis methods for uncertainty quantification
In this work we review a reduced basis method for the solution of uncertainty quantification
problems. Based on the basic setting of an elliptic partial differential equation with random …
problems. Based on the basic setting of an elliptic partial differential equation with random …
Mean-variance risk-averse optimal control of systems governed by PDEs with random parameter fields using quadratic approximations
We present a method for optimal control of systems governed by partial differential
equations (PDEs) with uncertain parameter fields. We consider an objective function that …
equations (PDEs) with uncertain parameter fields. We consider an objective function that …
[图书][B] Preconditioning and the conjugate gradient method in the context of solving PDEs
Our times can be characterized by, among many other attributes, the seemingly increasing
speed of everything. Within science, it has led to the publication explosion, which reflects the …
speed of everything. Within science, it has led to the publication explosion, which reflects the …
Existence and optimality conditions for risk-averse PDE-constrained optimization
DP Kouri, TM Surowiec - SIAM/ASA Journal on Uncertainty Quantification, 2018 - SIAM
Uncertainty is ubiquitous in virtually all engineering applications, and, for such problems, it is
inadequate to simulate the underlying physics without quantifying the uncertainty in …
inadequate to simulate the underlying physics without quantifying the uncertainty in …
Semiglobal optimal feedback stabilization of autonomous systems via deep neural network approximation
A learning approach for optimal feedback gains for nonlinear continuous time control
systems is proposed and analysed. The goal is to establish a rigorous framework for …
systems is proposed and analysed. The goal is to establish a rigorous framework for …
Inexact objective function evaluations in a trust-region algorithm for PDE-constrained optimization under uncertainty
This paper improves the trust-region algorithm with adaptive sparse grids introduced in
[SIAM J. Sci. Comput., 35 (2013), pp. A1847--A1879] for the solution of optimization …
[SIAM J. Sci. Comput., 35 (2013), pp. A1847--A1879] for the solution of optimization …
Complexity analysis of stochastic gradient methods for PDE-constrained optimal control problems with uncertain parameters
We consider the numerical approximation of an optimal control problem for an elliptic Partial
Differential Equation (PDE) with random coefficients. Specifically, the control function is a …
Differential Equation (PDE) with random coefficients. Specifically, the control function is a …