Graph Networks as Inductive Bias for Genetic Programming: Symbolic Models for Particle-Laden Flows
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Reuter:2023:EuroGP,
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author = "Julia Reuter and Hani Elmestikawy and
Fabien Evrard and Sanaz Mostaghim and Berend {van Wachem}",
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title = "Graph Networks as Inductive Bias for Genetic
Programming: Symbolic Models for Particle-Laden Flows",
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booktitle = "EuroGP 2023: Proceedings of the 26th European
Conference on Genetic Programming",
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year = "2023",
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month = "12-14 " # apr,
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editor = "Gisele Pappa and Mario Giacobini and Zdenek Vasicek",
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series = "LNCS",
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volume = "13986",
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publisher = "Springer Verlag",
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address = "Brno, Czech Republic",
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pages = "36--51",
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organisation = "EvoStar, Species",
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note = "Bronze 2023 HUMIES, EuroGP Best paper",
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keywords = "genetic algorithms, genetic programming, Graph
Networks, Fluid Mechanics",
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isbn13 = "978-3-031-29572-0",
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URL = "https://human-competitive.org/sites/default/files/reuter.txt",
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URL = "https://human-competitive.org/sites/default/files/paper2.pdf",
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URL = "https://rdcu.be/c8UOK",
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DOI = "doi:10.1007/978-3-031-29573-7_3",
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size = "16 pages",
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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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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