22 citations · 44 across the 7 of their papers we have counts for
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Taylor approximation for chance constrained optimization problems governed by partial differential equations with high-dimensional random parameters
Peng Chen, Omar Ghattas
We propose a fast and scalable optimization method to solve chance or probabilistic constrained optimization problems governed by partial differential equations (PDEs) with high-di…
Optimal design of acoustic metamaterial cloaks under uncertainty
Peng Chen, Michael R. Haberman, Omar Ghattas
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 problem. Th…
Projected Stein Variational Newton: A Fast and Scalable Bayesian Inference Method in High Dimensions
Peng Chen, Keyi Wu, Joshua Chen +2
We propose a fast and scalable variational method for Bayesian inference in high-dimensional parameter space, which we call projected Stein variational Newton (pSVN) method. We exp…
Taylor approximation and variance reduction for PDE-constrained optimal control under uncertainty
Peng Chen, Umberto Villa, Omar Ghattas
In this work we develop a scalable computational framework for the solution of PDE-constrained optimal control under high-dimensional uncertainty. Specifically, we consider a mean-…