A finite element-inspired hypergraph neural network: application to fluid dynamics simulations

DOI10.1016/J.JCP.2024.112866MaRDI QIDQ6497225

Indu Kant Deo, Rui Gao, Rajeev K. Jaiman

Publication date: 6 May 2024

Published in: Journal of Computational Physics (Search for Journal in Brave)

zbMATH Keywords

hypergraphfinite elementfluid dynamicsrotation equivariancegraph neural network

Mathematics Subject Classification ID

Turbulence (76Fxx) Numerical methods for partial differential equations, boundary value problems (65Nxx) Incompressible viscous fluids (76Dxx)

Cites Work

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• Gmsh: A 3-D finite element mesh generator with built-in pre- and post-processing facilities
• Turbulence and the dynamics of coherent structures. I. Coherent structures
• Finite Volume Methods for Hyperbolic Problems
• Spectral proper orthogonal decomposition and its relationship to dynamic mode decomposition and resolvent analysis
• Online Dynamic Mode Decomposition for Time-Varying Systems
• A conditional space–time POD formalism for intermittent and rare events: example of acoustic bursts in turbulent jets

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