Web: http://arxiv.org/abs/2205.02637

May 6, 2022, 1:11 a.m. | Mario Lino, Stathi Fotiadis, Anil A. Bharath, Chris Cantwell

cs.LG updates on arXiv.org arxiv.org

Numerical simulators are essential tools in the study of natural
fluid-systems, but their performance often limits application in practice.
Recent machine-learning approaches have demonstrated their ability to
accelerate spatio-temporal predictions, although, with only moderate accuracy
in comparison. Here we introduce MultiScaleGNN, a novel multi-scale graph
neural network model for learning to infer unsteady continuum mechanics in
problems encompassing a range of length scales and complex boundary geometries.
We demonstrate this method on advection problems and incompressible fluid
dynamics, both fundamental …

arxiv environmental graph graph neural networks networks neural neural networks physics scale simulation

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