62 citations · 178 across the 17 of their papers we have counts for
17 papers
Deep Gaussian Process Emulation and Uncertainty Quantification for Large Computer Experiments
Faezeh Yazdi, Derek Bingham, Daniel Williamson
Computer models are used as a way to explore complex physical systems. Stationary Gaussian process emulators, with their accompanying uncertainty quantification, are popular surrog…
Linked Deep Gaussian Process Emulation for Model Networks
Deyu Ming, Daniel Williamson
Modern scientific problems are often multi-disciplinary and require integration of computer models from different disciplines, each with distinct functional complexities, programmi…
Coexchangeable process modelling for uncertainty quantification in joint climate reconstruction
Lachlan Astfalck, Daniel Williamson, Niall Gandy +2
Any experiment with climate models relies on a potentially large set of spatio-temporal boundary conditions. These can represent both the initial state of the system and/or forcing…
Deep Gaussian Process Emulation using Stochastic Imputation
Deyu Ming, Daniel Williamson, Serge Guillas
Deep Gaussian processes (DGPs) provide a rich class of models that can better represent functions with varying regimes or sharp changes, compared to conventional GPs. In this work,…
Cross-validation based adaptive sampling for Gaussian process models
Hossein Mohammadi, Peter Challenor, Daniel Williamson +1
In many real-world applications, we are interested in approximating black-box, costly functions as accurately as possible with the smallest number of function evaluations. A comple…
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…