Learning Lagrangian fluid mechanics with E(3)-equivariant graph neural networks

DOI10.1007/978-3-031-38299-4_35arXiv2305.15603OpenAlexW4385434947MaRDI QIDQ6179021

Gianluca Galletti, Artur P. Toshev, Nikolaus A. Adams, Johannes Brandstetter, Stefan Adami

Publication date: 16 January 2024

Published in: Lecture Notes in Computer Science (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2305.15603

zbMATH Keywords

equivariancefluid mechanicssmoothed particle hydrodynamicsLagrangian methodsgraph neural networks

Mathematics Subject Classification ID

Particle methods and lattice-gas methods (76M28)

Cites Work

• A transport-velocity formulation for smoothed particle hydrodynamics
• A consistent multi-resolution smoothed particle hydrodynamics method
• Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• Smoothed particle hydrodynamics: theory and application to non-spherical stars
• Turbulent Flows
• JAX, M.D. A framework for differentiable physics*

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