171 citations · 898 across the 46 of their papers we have counts for
14 papers · 1 filter
Efficient PDE-Constrained optimization under high-dimensional uncertainty using derivative-informed neural operators
Dingcheng Luo, Thomas O'Leary-Roseberry, Peng Chen +1
We propose a novel machine learning framework for solving optimization problems governed by large-scale partial differential equations (PDEs) with high-dimensional random parameter…
Optimal design of chemoepitaxial guideposts for directed self-assembly of block copolymer systems using an inexact-Newton algorithm
Dingcheng Luo, Lianghao Cao, Peng Chen +2
Directed self-assembly (DSA) of block-copolymers (BCPs) is one of the most promising developments in the cost-effective production of nanoscale devices. The process makes use of th…
An efficient method for goal-oriented linear Bayesian optimal experimental design: Application to optimal sensor placemen
Keyi Wu, Peng Chen, Omar Ghattas
Optimal experimental design (OED) plays an important role in the problem of identifying uncertainty with limited experimental data. In many applications, we seek to minimize the un…
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…
Ill-Posedness and Optimization Geometry for Nonlinear Neural Network Training
Thomas O'Leary-Roseberry, Omar Ghattas
In this work we analyze the role nonlinear activation functions play at stationary points of dense neural network training problems. We consider a generic least squares loss functi…
Low Rank Saddle Free Newton: A Scalable Method for Stochastic Nonconvex Optimization
Thomas O'Leary-Roseberry, Nick Alger, Omar Ghattas
In modern deep learning, highly subsampled stochastic approximation (SA) methods are preferred to sample average approximation (SAA) methods because of large data sets as well as g…
Inexact Newton Methods for Stochastic Nonconvex Optimization with Applications to Neural Network Training
Thomas O'Leary-Roseberry, Nick Alger, Omar Ghattas
We study stochastic inexact Newton methods and consider their application in nonconvex settings. Building on the work of [R. Bollapragada, R. H. Byrd, and J. Nocedal, IMA Journal o…
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-…
A-optimal encoding weights for nonlinear inverse problems, with applications to the Helmholtz inverse problem
Benjamin Crestel, Alen Alexanderian, Georg Stadler +1
The computational cost of solving an inverse problem governed by PDEs, using multiple experiments, increases linearly with the number of experiments. A recently proposed method to…
Mean-variance risk-averse optimal control of systems governed by PDEs with random parameter fields using quadratic approximations
Alen Alexanderian, Noemi Petra, Georg Stadler +1
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 involve…
A Fast and Scalable Method for A-Optimal Design of Experiments for Infinite-dimensional Bayesian Nonlinear Inverse Problems
Alen Alexanderian, Noemi Petra, Georg Stadler +1
We address the problem of optimal experimental design (OED) for Bayesian nonlinear inverse problems governed by PDEs. The goal is to find a placement of sensors, at which experimen…
Scalable and efficient algorithms for the propagation of uncertainty from data through inference to prediction for large-scale problems, with application to flow of the Antarctic ice sheet
Tobin Isaac, Noemi Petra, Georg Stadler +1
The majority of research on efficient and scalable algorithms in computational science and engineering has focused on the forward problem: given parameter inputs, solve the governi…