@inproceedings{e1ba0d164e4943e4a730162c280c95b6,
title = "Identification of Dynamical Systems Using Symbolic Regression",
abstract = "We describe a method for the identification of models for dynamical systems from observational data. The method is based on the concept of symbolic regression and uses genetic programming to evolve a system of ordinary differential equations (ODE). The novelty is that we add a step of gradient-based optimization of the ODE parameters. For this we calculate the sensitivities of the solution to the initial value problem (IVP) using automatic differentiation. The proposed approach is tested on a set of 19 problem instances taken from the literature which includes datasets from simulated systems as well as datasets captured from mechanical systems. We find that gradient-based optimization of parameters improves predictive accuracy of the models. The best results are obtained when we first fit the individual equations to the numeric differences and then subsequently fine-tune the identified parameter values by fitting the IVP solution to the observed variable values.",
keywords = "Genetic programming, Symbolic regression, System dynamics",
author = "Gabriel Kronberger and Lukas Kammerer and Michael Kommenda",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 17th International Conference on Computer Aided Systems Theory, EUROCAST 2019 ; Conference date: 17-02-2019 Through 22-02-2019",
year = "2020",
doi = "10.1007/978-3-030-45093-9_45",
language = "English",
isbn = "9783030450922",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "370--377",
editor = "Roberto Moreno-D{\'i}az and Alexis Quesada-Arencibia and Franz Pichler",
booktitle = "Computer Aided Systems Theory – EUROCAST 2019 - 17th International Conference, Revised Selected Papers",
address = "Germany",
}