A higher-order singular value decomposition tensor emulator for spatio-temporal simulators

TL;DR

This paper presents a tensor emulator based on higher-order singular value decomposition (HOSVD) for spatio-temporal simulators, enabling flexible, accurate predictions at unseen locations and times, and integrating various machine learning methods.

Contribution

It introduces a novel HOSVD-based emulator that combines supervised learning techniques for improved spatio-temporal process modeling and prediction.

Findings

Successfully emulated glaciology PDE solutions.

Accurately modeled collective animal movement.

Enabled Bayesian inference for parameter learning.

Abstract

We introduce methodology to construct an emulator for environmental and ecological spatio-temporal processes that uses the higher order singular value decomposition (HOSVD) as an extension of singular value decomposition (SVD) approaches to emulation. Some important advantages of the method are that it allows for the use of a combination of supervised learning methods (e.g., random forests and Gaussian process regression) and also allows for the prediction of process values at spatial locations and time points that were not used in the training sample. The method is demonstrated with two applications: the first is a periodic solution to a shallow ice approximation partial differential equation from glaciology, and second is an agent-based model of collective animal movement. In both cases, we demonstrate the value of combining different machine learning models for accurate emulation. In addition, in the agent-based model case we demonstrate the ability of the tensor emulator to successfully capture individual behavior in space and time. We demonstrate via a real data example the ability to perform Bayesian inference in order to learn parameters governing collective animal behavior.