Constraining Gaussian Processes to Systems of Linear Ordinary Differential Equations. (arXiv:2208.12515v1 [cs.LG])

Data in many applications follows systems of Ordinary Differential Equations
(ODEs). This paper presents a novel algorithmic and symbolic construction for
covariance functions of Gaussian Processes (GPs) with realizations strictly
following a system of linear homogeneous ODEs with constant coefficients, which
we call LODE-GPs. Introducing this strong inductive bias into a GP improves
modelling of such data. Using smith normal form algorithms, a symbolic
technique, we overcome two current restrictions in the state of the art: (1)
the need for …

arxiv gaussian processes lg linear ordinary processes systems