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In: Advances in Neural Information Processing Systems 31 (NeurIPS 2018), Curran Associates

Algorithmic Linearly Constrained Gaussian Processes

Dec 2018

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, geomathematics and control, among them the full inhomogeneous system of Maxwell's equations. By bringing together stochastic learning and computer algebra in a novel way, we combine noisy observations with precise algebraic computations.

Literatur Beschaffung: Advances in Neural Information Processing Systems 31 (NeurIPS 2018), Curran Associates
@inproceedings{2381,
author= {Lange-Hegermann, Markus},
title= {Algorithmic Linearly Constrained Gaussian Processes},
abstract= {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, geomathematics and control, among them the full inhomogeneous system of Maxwell's equations. By bringing together stochastic learning and computer algebra in a novel way, we combine noisy observations with precise algebraic computations.},
booktitle= {Advances in Neural Information Processing Systems 31 (NeurIPS 2018)},
year= {2018},
month= {Dec},
publisher= {Curran Associates},
address= {https://arxiv.org/abs/1801.09197},
editor= {},
pages= {},
organisation= {},
}

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