Learning Dynamical Systems with Side Information (short version)

Amir Ali Ahmadi, Bachir El Khadir

Research output: Contribution to journalConference articlepeer-review

15 Scopus citations

Abstract

We present a mathematical formalism and a computational framework for the problem of learning a dynamical system from noisy observations of a few trajectories and subject to side information (e.g., physical laws or contextual knowledge). We identify six classes of side information which can be imposed by semidefinite programming and that arise naturally in many applications. We demonstrate their value on two examples from epidemiology and physics. Some density results on polynomial dynamical systems that either exactly or approximately satisfy side information are also presented.

Original languageEnglish (US)
Pages (from-to)718-727
Number of pages10
JournalProceedings of Machine Learning Research
Volume120
StatePublished - 2020
Event2nd Annual Conference on Learning for Dynamics and Control, L4DC 2020 - Berkeley, United States
Duration: Jun 10 2020Jun 11 2020

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Statistics and Probability

Keywords

  • Dynamical Systems
  • Learning
  • Semidefinite Programming
  • Sum of Squares Optimization

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