We describe and compare two formulations of inverse problems for a physics-based process
model in the context of uncertainty and random variability: the Bayesian inverse problem …

We propose a modeling paradigm, termed field inversion and machine learning (FIML), that
seeks to comprehensively harness data from sources such as high-fidelity simulations and …

To use predictive models in engineering design of physical systems, one should first
quantify the model uncertainty via model updating techniques employing both simulation …

This paper aims to provide new insights into model calibration, which plays an essential role
in improving the validity of Modeling and Simulation (M&S) in engineering design and …

In physics-based engineering modeling, the two primary sources of model uncertainty,
which account for the differences between computer models and physical experiments, are …

Our objective in this work is to provide a better understanding of the various model updating
strategies that utilize mathematical means to update a computer model based on both …

In this paper we introduce GP+, an open-source library for kernel-based learning via
Gaussian processes (GPs) which are powerful statistical models that are completely …

Computer simulators are widely used to describe and explore physical processes. In some
cases, several simulators are available, each with a different degree of fidelity, for this task …

We consider adapting a canonical computer model calibration apparatus, involving coupled
Gaussian process (GP) emulators, to a computer experiment simulating radiative shock …

In an effort to speed the development of new products and processes, many companies are
turning to computer simulations to avoid the time and expense of building prototypes. These …