Adjoint-aided inference of Gaussian process driven differential equations

Paterne Gahungu, Christopher W Lanyon, Mauricio A Álvarez, Engineer Bainomugisha, Michael Thomas Smith, Richard David Wilkinson

Keywords: Uncertainty quantification, latent force, Gaussian process, differential equation, inverse problem

TL;DR: Gaussian process forcing functions can be cheaply inferred in a conjugate analysis using an adjoint formulation of the linear systems.

Abstract: Linear systems occur throughout engineering and the sciences, most notably as differential equations. In many cases the forcing function for the system is unknown, and interest lies in using noisy observations of the system to infer the forcing, as well as other unknown parameters. In differential equations, the forcing function is an unknown function of the independent variables (typically time and space), and can be modelled as a Gaussian process (GP). In this paper we show how the adjoint of a linear system can be used to efficiently infer forcing functions modelled as GPs, after using a truncated basis expansion of the GP kernel. We show how exact conjugate Bayesian inference for the truncated GP can be achieved, in many cases with substantially lower computation than would be required using MCMC methods. We demonstrate the approach on systems of both ordinary and partial differential equations, and show that the basis expansion approach approximates well the true forcing with a modest number of basis vectors. Finally, we show how to infer point estimates for the non-linear model parameters, such as the kernel length-scales, using Bayesian optimisation.