Towards Fast Simulation of Environmental Fluid Mechanics with Multi-Scale Graph Neural Networks
This work addresses the need for faster and accurate fluid dynamics simulations in oceanic and atmospheric processes, representing an incremental improvement over existing machine-learning approaches.
The paper tackles the problem of slow numerical simulators for environmental fluid mechanics by introducing MultiScaleGNN, a multi-scale graph neural network model, which achieves simulations 2-4 orders of magnitude faster than training data while maintaining good extrapolation to new geometries and parameters.
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 phenomena in oceanic and atmospheric processes. Our results show good extrapolation to new domain geometries and parameters for long-term temporal simulations. Simulations obtained with MultiScaleGNN are between two and four orders of magnitude faster than those on which it was trained.