NIPS Proceedingsβ

Algorithmic Linearly Constrained Gaussian Processes

Part of: Advances in Neural Information Processing Systems 31 (NIPS 2018)

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Conference Event Type: Poster


We algorithmically construct multi-output Gaussian process priors which satisfy linear differential equations. Our approach attempts to parametrize all solutions of the equations using Gröbner bases. If successful, a push forward Gaussian process along the paramerization is the desired prior. We consider several examples from physics, geomathmatics and control, among them the full inhomogeneous system of Maxwell's equations. By bringing together stochastic learning and computeralgebra in a novel way, we combine noisy observations with precise algebraic computations.