Deep learning for gradient flows using the Brezis–Ekeland principle

We propose a deep learning method for the numerical solution of partial differential equations that arise as gradient flows. The relies on Brezis–Ekeland principle, which naturally defines an objective function to be minimized, and so is ideally suited machine approach using neural networks. describe our in general framework illustrate with help example implementation heat equation space dimensions two seven.