Stein's method compares probability distributions through the study of a class of linear
operators called Stein operators. While mainly studied in probability and used to underpin …

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 …

Bayesian inference problems require sampling or approximating high-dimensional
probability distributions. The focus of this paper is on the recently introduced Stein …

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

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

We introduce a new mean-field ODE and corresponding interacting particle systems (IPS)
for sampling from an unnormalized target density. The IPS are gradient-free, available in …

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 explore using neural operators, or neural network representations of nonlinear maps
between function spaces, to accelerate infinite-dimensional Bayesian inverse problems …

Most scientific machine learning (SciML) applications of neural networks involve hundreds
to thousands of parameters, and hence, uncertainty quantification for such models is …