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Graph Networks as Inductive Bias for Genetic Programming: Symbolic Models for Particle-Laden Flows

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Genetic Programming (EuroGP 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13986))

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Abstract

High-resolution simulations of particle-laden flows are computationally limited to a scale of thousands of particles due to the complex interactions between particles and fluid. Some approaches to increase the number of particles in such simulations require information about the fluid-induced force on a particle, which is a major challenge in this research area. In this paper, we present an approach to develop symbolic models for the fluid-induced force. We use a graph network as inductive bias to model the underlying pairwise particle interactions. The internal parts of the network are then replaced by symbolic models using a genetic programming algorithm. We include prior problem knowledge in our algorithm. The resulting equations show an accuracy in the same order of magnitude as state-of-the-art approaches for different benchmark datasets. They are interpretable and deliver important building blocks. Our approach is a promising alternative to “black-box” models from the literature.

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Authors and Affiliations

  1. Institute for Intelligent Cooperating Systems, Otto-von-Guericke-University, Magdeburg, Germany

    Julia Reuter & Sanaz Mostaghim

  2. Institute for Mechanical Process Engineering, Otto-von-Guericke-University, Magdeburg, Germany

    Hani Elmestikawy, Fabien Evrard & Berend van Wachem

Authors
  1. Julia Reuter
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  2. Hani Elmestikawy
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  3. Fabien Evrard
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  4. Sanaz Mostaghim
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  5. Berend van Wachem
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Corresponding author

Correspondence to Julia Reuter .

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Editors and Affiliations

  1. Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil

    Gisele Pappa

  2. Università degli studi di Torino, Turin, Italy

    Mario Giacobini

  3. Brno University of Technology, Brno, Czech Republic

    Zdenek Vasicek

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Reuter, J., Elmestikawy, H., Evrard, F., Mostaghim, S., van Wachem, B. (2023). Graph Networks as Inductive Bias for Genetic Programming: Symbolic Models for Particle-Laden Flows. In: Pappa, G., Giacobini, M., Vasicek, Z. (eds) Genetic Programming. EuroGP 2023. Lecture Notes in Computer Science, vol 13986. Springer, Cham. https://doi.org/10.1007/978-3-031-29573-7_3

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  • DOI: https://doi.org/10.1007/978-3-031-29573-7_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-29572-0

  • Online ISBN: 978-3-031-29573-7

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