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

Code

• github.com/wenkph/FGPGM
  OfficialIn papernone★ 0
• github.com/ngorbach/Variational_Gradient_Matching_for_Dynamical_Systems
  none★ 0
• github.com/jessiedbq/Auto_Sci-master
  pytorch★ 0

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.

Tasks

Reproductions