Computer simulation experiments are essential to modern scientific discovery, whether that
be in physics, chemistry, biology, epidemiology, ecology, engineering, etc. Surrogates are …

Abstract Model calibration is crucial for optimizing the performance of complex computer
models across various disciplines. In the era of Industry 4.0, symbolizing rapid technological …

Simulation models of critical systems often have parameters that need to be calibrated using
observed data. For expensive simulation models, calibration is done using an emulator of …

Gaussian process surrogates are a popular alternative to directly using computationally
expensive simulation models. When the simulation output consists of many responses …

The Kennedy and O'Hagan (KOH) calibration framework uses coupled Gaussian processes
(GPs) to meta-model an expensive simulator (first GP), tune its' knobs'(calibration inputs) to …

We propose a Bayesian approach for parameter uncertainty quantification in macroscopic
traffic flow models from cross-sectional data. We consider both a simple first order model …

Surrogate modeling and active subspaces have emerged as powerful paradigms in
computational science and engineering. Porting such techniques to computational models …

Simulation models often have parameters as input and return outputs to understand the
behavior of complex systems. Calibration is the process of estimating the values of the …

Abstract The Kennedy and O'Hagan (KOH) calibration framework uses coupled Gaussian
processes (GPs) to meta-model an expensive simulator (first GP), tune its “knobs”(calibration …

This thesis concerns the construction and analysis of emulators for high-dimensional
computer simulation outputs. The construction of emulators is beneficial when computer …