Priors for Linear Differential Equations

Abstract

We algorithmically construct multi-output Gaussian process priors which satisfy linear differential equations. We parametrize all solutions of the differential equations using Gröbner bases for controllable systems. If successful, a push forward along the parametrization is the desired prior. This prior yields an interpretable machine learning model, which can combine linear differential equations with noisy data points.