In many situations across computational science and engineering, multiple computational
models are available that describe a system of interest. These different models have varying …

In computational fluid dynamics simulations of industrial flows, models based on the
Reynolds-averaged Navier–Stokes (RANS) equations are expected to play an important …

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 …

Parameter studies are everywhere in computational science. Complex engineering
simulations must run several times with different inputs to effectively study the relationships …

Despite their well-known limitations, Reynolds-Averaged Navier–Stokes (RANS) models are
still the workhorse tools for turbulent flow simulations in today's engineering analysis, design …

Many Bayesian inference problems require exploring the posterior distribution of high-
dimensional parameters that represent the discretization of an underlying function. This work …

The basic idea of importance sampling is to use independent samples from a proposal
measure in order to approximate expectations with respect to a target measure. It is key to …

Bayesian inverse problems often involve sampling posterior distributions on infinite-
dimensional function spaces. Traditional Markov chain Monte Carlo (MCMC) algorithms are …

Inverse problems deal with the quest for unknown causes of observed consequences,
based on predictive models, known as the forward models, that associate the former …