most citedA Finite Element-Inspired Hypergraph Neural Network: Application to Fluid Dynamics Simulations

32 citations · 60 across the 3 of their papers we have counts for

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physics.flu-dyn2022★ 32 cited

A Finite Element-Inspired Hypergraph Neural Network: Application to Fluid Dynamics Simulations

Rui Gao, Indu Kant Deo, Rajeev K. Jaiman

An emerging trend in deep learning research focuses on the applications of graph neural networks (GNNs) for mesh-based continuum mechanics simulations. Most of these learning frame…

physics.flu-dyn2022★ 26 cited

Predicting fluid-structure interaction with graph neural networks

Rui Gao, Rajeev K. Jaiman

We present a rotation equivariant, quasi-monolithic graph neural network framework for the reduced-order modeling of fluid-structure interaction systems. With the aid of an arbitra…