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

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Reuter:2023:EuroGP,

• author = "Julia Reuter and Hani Elmestikawy and Fabien Evrard and Sanaz Mostaghim and Berend {van Wachem}",
• title = "Graph Networks as Inductive Bias for Genetic Programming: Symbolic Models for Particle-Laden Flows",
• booktitle = "EuroGP 2023: Proceedings of the 26th European Conference on Genetic Programming",
• year = "2023",
• month = "12-14 " # apr,
• editor = "Gisele Pappa and Mario Giacobini and Zdenek Vasicek",
• series = "LNCS",
• volume = "13986",
• publisher = "Springer Verlag",
• address = "Brno, Czech Republic",
• pages = "36--51",
• organisation = "EvoStar, Species",
• note = "Bronze 2023 HUMIES, EuroGP Best paper",
• keywords = "genetic algorithms, genetic programming, Graph Networks, Fluid Mechanics",
• isbn13 = "978-3-031-29572-0",
• URL = "https://human-competitive.org/sites/default/files/reuter.txt",
• URL = "https://human-competitive.org/sites/default/files/paper2.pdf",
• URL = "https://rdcu.be/c8UOK",
• DOI = "doi:10.1007/978-3-031-29573-7_3",
• size = "16 pages",
• 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.",
• notes = "Part of \cite{Pappa:2023:GP} EuroGP'2023 held in conjunction with EvoCOP2023, EvoMusArt2023 and EvoApplications2023",

}

Genetic Programming entries for Julia Reuter Hani Elmestikawy Fabien Evrard Sanaz Mostaghim Berend van Wachem

Citations