171 citations · 898 across the 46 of their papers we have counts for
4 papers · 1 filter
hIPPYlib: An Extensible Software Framework for Large-Scale Inverse Problems Governed by PDEs; Part I: Deterministic Inversion and Linearized Bayesian Inference
Umberto Villa, Noemi Petra, Omar Ghattas
We present an extensible software framework, hIPPYlib, for solution of large-scale deterministic and Bayesian inverse problems governed by partial differential equations (PDEs) wit…
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…
Sparse polynomial approximation for optimal control problems constrained by elliptic PDEs with lognormal random coefficients
Peng Chen, Omar Ghattas
In this work, we consider optimal control problems constrained by elliptic partial differential equations (PDEs) with lognormal random coefficients, which are represented by a coun…
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…