Fast Gaussian process based gradient matching for parameter identification in systems of nonlinear ODEs

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Philippe Wenk, Alkis Gotovos, Stefan Bauer, Nico S. Gorbach, Andreas Krause, Joachim M. Buhmann ;
Proceedings of Machine Learning Research, PMLR 89:1351-1360, 2019.

Abstract

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, the theoretical foundation has been criticized in the past. We offer a novel interpretation which leads to a better understanding, improvements in state-of-the-art performance in terms of accuracy and robustness and a decrease in run time due to a more efficient setup for general nonlinear dynamical systems.

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