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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 and improvements in state-of-the-art performance in terms of accuracy, robustness and a decrease in run time due to a more efficient setup for general nonlinear dynamical systems.
Reference:
Fast Gaussian process based gradient matching for parameter identification in systems of nonlinear ODEs P. Wenk, A. Gotovos, S. Bauer, N. S. Gorbach, A. Krause, J. M. BuhmannIn International Conference on Artificial Intelligence and Statistics (AISTATS), 2019
Bibtex Entry:
@inproceedings{wenk18fgpgm,
	Author = {Wenk, Philippe and Gotovos, Alkis and Bauer, Stefan and Gorbach, Nico S. and Krause, Andreas and Buhmann, Joachim M.},
	Booktitle = {International Conference on Artificial Intelligence and Statistics (AISTATS)},
	Month = {April},
	Title = {Fast Gaussian process based gradient matching for parameter identification in systems of nonlinear ODEs},
	Year = {2019}}