DOLFINx is the next generation problem solving environment from the FEniCS Project; it
provides an expressive and performant environment for solving partial differential equations …

Questions of 'how best to acquire data'are essential to modelling and prediction in the
natural and social sciences, engineering applications, and beyond. Optimal experimental …

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 …

This paper surveys an important class of methods that combine iterative projection methods
and variational regularization methods for large-scale inverse problems. Iterative methods …

Earth system models (ESMs) are the primary tools for investigating future Earth system
states at timescales from decades to centuries, especially in response to anthropogenic …

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 …

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

We propose a scalable framework for the learning of high-dimensional parametric maps via
adaptively constructed residual network (ResNet) maps between reduced bases of the …

We consider a mixture-theoretic continuum model of the spread of COVID-19 in Texas. The
model consists of multiple coupled partial differential reaction–diffusion equations governing …