A Feedforward Unitary Equivariant Neural Network

08/25/2022

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by   Pui-Wai Ma, et al.

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We devise a new type of feedforward neural network. It is equivariant with respect to the unitary group U(n). The input and output can be vectors in ℂ^n with arbitrary dimension n. No convolution layer is required in our implementation. We avoid errors due to truncated higher order terms in Fourier-like transformation. The implementation of each layer can be done efficiently using simple calculations. As a proof of concept, we have given empirical results on the prediction of the dynamics of atomic motion to demonstrate the practicality of our approach.