This article addresses the inference of physics models from data, from the perspectives of
inverse problems and model reduction. These fields develop formulations that integrate data …

We present an extensible software framework, hIPPYlib, for solution of large-scale
deterministic and Bayesian inverse problems governed by partial differential equations …

Many-query problems–arising from, eg, uncertainty quantification, Bayesian inversion,
Bayesian optimal experimental design, and optimization under uncertainty–require …

We propose derivative-informed neural operators (DINOs), a general family of neural
networks to approximate operators as infinite-dimensional map**s from input function …

The curse of dimensionality is a longstanding challenge in Bayesian inference in high
dimensions. In this work, we propose a {projected Stein variational gradient …

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 …

We develop a fast and scalable computational framework to solve Bayesian optimal
experimental design problems governed by partial differential equations (PDEs) with …

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 …

We propose a projected Stein variational Newton (pSVN) method for high-dimensional
Bayesian inference. To address the curse of dimensionality, we exploit the intrinsic low …

We study the application of a tailored quasi-Monte Carlo (QMC) method to a class of optimal
control problems subject to parabolic partial differential equation (PDE) constraints under …