Quantifying Uncertainty for loss of coolant in a nuclear reactor in Local Approximate Gaussian Processes with a Structured Global Mean Function

Music City Center 

Gaussian process (GP) models are commonly used in the field of surrogate modeling due to their ability to provide a predictive distribution, interpolate in deterministic settings, and provide a foundation for uncertainty. However, in cases where the number of observations in the training set is large, the ability to model using a GP becomes computationally intractable. As such, fast approximations of such models have become prominent throughout GP literature. One of the leading approaches is to construct a GP using a local neighbourhood for each testing point, which has come to be known as a local approximate GP). While this approach performs well in cases where the number of inputs is small, it may falter when this is not the case. In this work, we propose to use local Gaussian processes with structured mean functions. The proposed approach is used to emulate a loss of coolant model for a CANDU nuclear reactor. Issues of internal extrapolation in sparse sampling case naturally arises.

Local Gaussian process

Computer experiments

Extrapolation