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

Philippe Wenk, Alkis Gotovos, Stefan Bauer, Nico Gorbach, Andreas Krause +1 more
4/12/2018
stat.MLcs.LG

Official code: wenkph/FGPGM (7 stars, last commit 2019-03-01).

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, 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 Implementations(1)

wenkph/FGPGMOfficial100%

Implementation of FGPGM in python 2.7, based on the paper "Fast Gaussian Process Based Gradient Matching for Parameter Identification in Systems of Nonlinear ODEs" by Philippe Wenk, Alkis Gotovos, Stefan Bauer, Nico Gorbach, Andreas Krause and Joachim M. Buhmann. If you use this code for further research, please consider citing our paper (http://arxiv.org/abs/1804.04378 ).

73PythonApr 11, 20187 years ago

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Cite this paper

@article{wenk2018fast,
  title  = {Fast Gaussian Process Based Gradient Matching for Parameter Identification in Systems of Nonlinear ODEs},
  author = {Philippe Wenk and Alkis Gotovos and Stefan Bauer and Nico Gorbach and Andreas Krause and Joachim M. Buhmann},
  year   = {2018},
  eprint = {1804.04378},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1804.04378}
}

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