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.
CITATION STYLE
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 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13986 LNCS, pp. 36–51). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-29573-7_3
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