Year: 2018
Type: preprint
Abstract: Weakly stationary Gaussian processes (GPs) are the principal tool in the statistical approaches to the design and analysis of computer experiments (or Uncertainty Quantification). Such processes are fitted to computer model output using a set of training runs to learn the parameters of the process covariance kernel. The stationarity assumption is often adequate, yet can lead to poor predictive performance when the model response exhibits nonstationarity, for example, if its smoothness varies across the input space. In this paper, we introduce a diagnostic-led approach to fitting nonstationary GP emulators by specifying finite mixtures of region-specific covariance kernels. Our method first fits a stationary GP and, if traditional diagnostics exhibit nonstationarity, those diagnostics are used to fit appropriate mixing functions for a covariance kernel mixture designed to capture the nonstationarity, ensuring an emulator that is continuous in parameter space and readily interpretable. We compare our approach to the principal nonstationary GP models in the literature and illustrate its performance on a number of idealised test cases and in an application to modelling the cloud parameterization of the French climate model.
Source: arXiv (Cornell University)
Authors: Volodina, Victoria, Williamson, Daniel B.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: Uncertainty quantification for the linking of spatio-temporal output of computer model hierarchies and the real world, High-resolution simulations to improve and tune the boundary-layer cloud parametrizations, G3026286742, Persistence of seasonal Climate Anomalies: Drivers, Mechanisms and Process-based Diagnostics, G4798079185, G5420063140
OpenAlex ID: W3008702160