Machine Learning Assisted Mesh Adaptation for Geophysical Fluid Dynamics
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Numerical simulations play a central role in understanding the impact and risks of pressing global problems, particularly to the scale-up challenges of energy generation from complex, non-linear sources such as wind and tidal. Effectively discretizing the extreme spatial scale variability inherent in such geophysical fluid dynamics problems can come at a high computational cost when targeting a reasonable level of accuracy for meaningful results. Mesh adaptation is the process of modifying the discretized structure to improve the accuracy of numerical simulations. In addition to mesh adaptation, identifying opportunities to augment the numerical methods, particularly iterative methods, with machine learning workflows has potential to further reduce computational overhead by automating the process and incorporating prior knowledge. In this talk, we review our work applying machine learning for mesh adaptation in numerical simulations motivated by tidal energy applications. We focus the discussion on trade-offs between accuracy preservation and efficiency gain for machine learning-based mesh adaptation methods that utilizes a combination of supervised and unsupervised learning techniques. A series of simple benchmark applications will be presented.
