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Learning Dynamical Systems Using Standard Symbolic Regression

  • Sébastien Gaucel
  • Maarten Keijzer
  • Evelyne Lutton
  • Alberto Tonda
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8599)

Abstract

Symbolic regression has many successful applications in learning free-form regular equations from data. Trying to apply the same approach to differential equations is the logical next step: so far, however, results have not matched the quality obtained with regular equations, mainly due to additional constraints and dependencies between variables that make the problem extremely hard to tackle. In this paper we propose a new approach to dynamic systems learning. Symbolic regression is used to obtain a set of first-order Eulerian approximations of differential equations, and mathematical properties of the approximation are then exploited to reconstruct the original differential equations. Advantages of this technique include the de-coupling of systems of differential equations, that can now be learned independently; the possibility of exploiting established techniques for standard symbolic regression, after trivial operations on the original dataset; and the substantial reduction of computational effort, when compared to existing ad-hoc solutions for the same purpose. Experimental results show the efficacy of the proposed approach on an instance of the Lotka-Volterra model.

Keywords

Differential Equations Dynamic Systems Evolutionary Algorithms Genetic Programming Symbolic Regression 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Sébastien Gaucel
    • 1
  • Maarten Keijzer
    • 2
  • Evelyne Lutton
    • 1
  • Alberto Tonda
    • 1
  1. 1.INRA UMR 782 GMPAThiverval-GrignonFrance
  2. 2.Pegasystems Inc.AmsterdamThe Netherlands

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