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
Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, 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.

Cite this Paper

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.} }
Endnote
%0 Conference Paper %T Fast Gaussian process based gradient matching for parameter identification in systems of nonlinear ODEs %A Philippe Wenk %A Alkis Gotovos %A Stefan Bauer %A Nico S. Gorbach %A Andreas Krause %A Joachim M. Buhmann %B Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2019 %E Kamalika Chaudhuri %E Masashi Sugiyama %F pmlr-v89-wenk19a %I PMLR %P 1351--1360 %U https://proceedings.mlr.press/v89/wenk19a.html %V 89 %X 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.
APA
Wenk, P., Gotovos, A., Bauer, S., Gorbach, N.S., Krause, A. & Buhmann, J.M.. (2019). Fast Gaussian process based gradient matching for parameter identification in systems of nonlinear ODEs. Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 89:1351-1360 Available from https://proceedings.mlr.press/v89/wenk19a.html.

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