22 citations · 53 across the 6 of their papers we have counts for
16 papers
A stochastic Stein Variational Newton method
Alex Leviyev, Joshua Chen, Yifei Wang +2
Stein variational gradient descent (SVGD) is a general-purpose optimization-based sampling algorithm that has recently exploded in popularity, but is limited by two issues: it is k…
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…
Bayesian inference of heterogeneous epidemic models: Application to COVID-19 spread accounting for long-term care facilities
Peng Chen, Keyi Wu, Omar Ghattas
We propose a high dimensional Bayesian inference framework for learning heterogeneous dynamics of a COVID-19 model, with a specific application to the dynamics and severity of COVI…
Derivative-Informed Projected Neural Networks for High-Dimensional Parametric Maps Governed by PDEs
Thomas O'Leary-Roseberry, Umberto Villa, Peng Chen +1
Many-query problems, arising from uncertainty quantification, Bayesian inversion, Bayesian optimal experimental design, and optimization under uncertainty-require numerous evaluati…
A fast and scalable computational framework for large-scale and high-dimensional Bayesian optimal experimental design
Keyi Wu, Peng Chen, Omar Ghattas
We develop a fast and scalable computational framework to solve large-scale and high-dimensional Bayesian optimal experimental design problems. In particular, we consider the probl…
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…