Learning Constrained Dynamics with Gauss Principle adhering Gaussian Processes

• URL: http://arxiv.org/abs/2004.11238v1
• Date: Thu, 23 Apr 2020 15:26:51 GMT
• Title: Learning Constrained Dynamics with Gauss Principle adhering Gaussian Processes
• Authors: A. Rene Geist and Sebastian Trimpe
• Abstract summary: We propose to combine insights from analytical mechanics with Gaussian process regression to improve the model's data efficiency and constraint integrity. Our model enables to infer the acceleration of the unconstrained system from data of the constrained system.
• Score: 7.643999306446022
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The identification of the constrained dynamics of mechanical systems is often challenging. Learning methods promise to ease an analytical analysis, but require considerable amounts of data for training. We propose to combine insights from analytical mechanics with Gaussian process regression to improve the model's data efficiency and constraint integrity. The result is a Gaussian process model that incorporates a priori constraint knowledge such that its predictions adhere to Gauss' principle of least constraint. In return, predictions of the system's acceleration naturally respect potentially non-ideal (non-)holonomic equality constraints. As corollary results, our model enables to infer the acceleration of the unconstrained system from data of the constrained system and enables knowledge transfer between differing constraint configurations.

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