ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical Systems

  • Philippe Wenk ETH Zurich
  • Gabriele Abbati University of Oxford
  • Michael A. Osborne University of Oxford
  • Bernhard Schölkopf Max Planck Institute for Intelligent Systems
  • Andreas Krause ETH Zurich
  • Stefan Bauer MPI IS

Abstract

Parameter inference in ordinary differential equations is an important problem in many applied sciences and in engineering, especially in a data-scarce setting. In this work, we introduce a novel generative modeling approach based on constrained Gaussian processes and leverage it to build a computationally and data efficient algorithm for state and parameter inference. In an extensive set of experiments, our approach outperforms the current state of the art for parameter inference both in terms of accuracy and computational cost. It also shows promising results for the much more challenging problem of model selection.

Published
2020-04-03
Section
AAAI Technical Track: Machine Learning