138 citations · 138 across the 5 of their papers we have counts for
5 papers · 1 filter
Cross-Validation Based Adaptive Sampling for Multi-Level Gaussian Process Models
Louise Kimpton, James Salter, Tim Dodwell +2
Complex computer codes or models can often be run in a hierarchy of different levels of complexity ranging from the very basic to the sophisticated. The top levels in this hierarch…
Classification of Computer Models with Labelled Outputs
Louise Kimpton, Peter Challenor, Daniel Williamson
Classification is a vital tool that is important for modelling many complex numerical models. A model or system may be such that, for certain areas of input space, the output eithe…
Emulating computer models with step-discontinuous outputs using Gaussian processes
Hossein Mohammadi, Peter Challenor, Marc Goodfellow +1
In many real-world applications we are interested in approximating costly functions that are analytically unknown, e.g. complex computer codes. An emulator provides a fast approxim…
Predicting the Output From a Stochastic Computer Model When a Deterministic Approximation is Available
Evan Baker, Peter Challenor, Matt Eames
The analysis of computer models can be aided by the construction of surrogate models, or emulators, that statistically model the numerical computer model. Increasingly, computer mo…
Diagnostics for Stochastic Gaussian Process Emulators
Evan Baker, Peter Challenor, Matt Eames
Computer models, also known as simulators, can be computationally expensive to run, and for this reason statistical surrogates, known as emulators, are often used. Any statistical…