Recurrent Neural Networks as Optimal Mesh Refinement Strategies

09/10/2019

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by   Jan Bohn, et al.

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We show that an optimal finite element mesh refinement algorithm for a prototypical elliptic PDE can be learned by a recurrent neural network with a fixed number of trainable parameters independent of the desired accuracy and the input size, i.e., number of elements of the mesh.