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

Numerical simulation of large-scale dynamical systems plays a fundamental role in studying
a wide range of complex physical phenomena; however, the inherent large-scale nature of …

In the atmosphere, microphysics refers to the microscale processes that affect cloud and
precipitation particles and is a key linkage among the various components of Earth's …

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 …

The spatially and temporally variable parameters and inputs to complex groundwater
models typically result in long runtimes which hinder comprehensive calibration, sensitivity …

This book results from a course developed by the author and reflects both his own and
collaborative research regarding the development and implementation of uncertainty …

Monte Carlo simulation (MCS) provides a conceptually simple and robust method to
evaluate the system reliability of slope stability, particularly in spatially variable soils …

We address the solution of large-scale statistical inverse problems in the framework of
Bayesian inference. The Markov chain Monte Carlo (MCMC) method is the most popular …

Spatially distributed hydrologic models are increasingly being used to study and predict soil
moisture flow, groundwater recharge, surface runoff, and river discharge. The usefulness …

The optimal selection of experimental conditions is essential to maximizing the value of data
for inference and prediction, particularly in situations where experiments are time …