Web: http://arxiv.org/abs/2206.07681

June 16, 2022, 1:11 a.m. | Tailin Wu, Takashi Maruyama, Jure Leskovec

cs.LG updates on arXiv.org arxiv.org

Simulating the time evolution of Partial Differential Equations (PDEs) of
large-scale systems is crucial in many scientific and engineering domains such
as fluid dynamics, weather forecasting and their inverse optimization problems.
However, both classical solvers and recent deep learning-based surrogate models
are typically extremely computationally intensive, because of their local
evolution: they need to update the state of each discretized cell at each time
step during inference. Here we develop Latent Evolution of PDEs (LE-PDE), a
simple, fast and scalable …

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