Known Boundary Emulation of Complex Computer Models

TL;DR

This paper introduces a Bayesian updating method for Gaussian process emulators to incorporate known boundaries in the input space, significantly improving accuracy and efficiency in complex model emulation.

Contribution

It presents a novel analytical approach to include known boundaries in Gaussian process emulators, enhancing their accuracy without additional computational cost.

Findings

Increased emulator accuracy across large input regions.

Method easily integrated into existing GP emulation packages.

Effective application demonstrated on a biological system model.

Abstract

Computer models are now widely used across a range of scientific disciplines to describe various complex physical systems, however to perform full uncertainty quantification we often need to employ emulators. An emulator is a fast statistical construct that mimics the complex computer model, and greatly aids the vastly more computationally intensive uncertainty quantification calculations that a serious scientific analysis often requires. In some cases, the complex model can be solved far more efficiently for certain parameter settings, leading to boundaries or hyperplanes in the input parameter space where the model is essentially known. We show that for a large class of Gaussian process style emulators, multiple boundaries can be formally incorporated into the emulation process, by Bayesian updating of the emulators with respect to the boundaries, for trivial computational cost. The resulting updated emulator equations are given analytically. This leads to emulators that possess increased accuracy across large portions of the input parameter space. We also describe how a user can incorporate such boundaries within standard black box GP emulation packages that are currently available, without altering the core code. Appropriate designs of model runs in the presence of known boundaries are then analysed, with two kinds of general purpose designs proposed. We then apply the improved emulation and design methodology to an important systems biology model of hormonal crosstalk in Arabidopsis Thaliana.