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Fast Gaussian Process Based Gradient Matching for Parameter Identification in Systems of Nonlinear ODEs

by   Philippe Wenk, et al.

Parameter identification and comparison of dynamical systems is a challenging task in many fields. Bayesian approaches based on Gaussian process regression over time-series data have been successfully applied to infer the parameters of a dynamical system without explicitly solving it. While the benefits in computational cost are well established, a rigorous mathematical framework has been missing. We offer a novel interpretation which leads to a better understanding and improvements in state-of-the-art performance in terms of accuracy for nonlinear dynamical systems.


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Code Repositories


Sample code for the NIPS paper "Scalable Variational Inference for Dynamical Systems"

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