Fast Gaussian process based gradient matching for parameter identification in systems of nonlinear ODEs
Philippe Wenk, Alkis Gotovos, Stefan Bauer, Nico S. Gorbach, Andreas Krause, Joachim M. Buhmann
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.
BibTeX
@InProceedings{pmlr-v89-wenk19a,
title = {Fast Gaussian process based gradient matching for parameter identification in systems of nonlinear ODEs},
author = {Wenk, Philippe and Gotovos, Alkis and Bauer, Stefan and Gorbach, Nico S. and Krause, Andreas and Buhmann, Joachim M.},
booktitle = {Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics},
pages = {1351--1360},
year = {2019},
editor = {Chaudhuri, Kamalika and Sugiyama, Masashi},
volume = {89},
series = {Proceedings of Machine Learning Research},
month = {16--18 Apr},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v89/wenk19a/wenk19a.pdf},
url = {https://proceedings.mlr.press/v89/wenk19a.html},
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.}
}