Lange-Hegermann, MarkusDavid, KlausGeihs, KurtLange, MartinStumme, Gerd2019-08-272019-08-272019978-3-88579-688-6https://dl.gi.de/handle/20.500.12116/24986We 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.enGaussian processregressiondifferential equationkernelGröbner basisText/Conference Paper10.18420/inf2019_381617-5468