Invariant deep neural networks under the finite group for solving partial differential equations

TL;DR

This paper introduces a symmetry-enhanced neural network architecture that incorporates finite group invariance to improve the accuracy and generalization of physics-informed neural networks for solving PDEs.

Contribution

The paper proposes a novel symmetry-enhanced deep neural network (sDNN) that maintains invariance under finite groups, reducing parameters and enhancing prediction beyond sampling domains.

Findings

sDNN outperforms vanilla PINN in accuracy and generalization

sDNN requires fewer training points and has a simpler architecture

The architecture is proven to be invariant under finite groups

Abstract

Utilizing physics-informed neural networks (PINN) to solve partial differential equations (PDEs) becomes a hot issue and also shows its great powers, but still suffers from the dilemmas of limited predicted accuracy in the sampling domain and poor prediction ability beyond the sampling domain which are usually mitigated by adding the physical properties of PDEs into the loss function or by employing smart techniques to change the form of loss function for special PDEs. In this paper, we design a symmetry-enhanced deep neural network (sDNN) which makes the architecture of neural networks invariant under the finite group through expanding the dimensions of weight matrixes and bias vectors in each hidden layers by the order of finite group if the group has matrix representations, otherwise extending the set of input data and the hidden layers except for the first hidden layer by the order of finite group. However, the total number of training parameters is only about one over the order of finite group of the original PINN size due to the symmetric architecture of sDNN. Furthermore, we give special forms of weight matrixes and bias vectors of sDNN, and rigorously prove that the architecture itself is invariant under the finite group and the sDNN has the universal approximation ability to learn the function keeping the finite group. Numerical results show that the sDNN has strong predicted abilities in and beyond the sampling domain and performs far better than the vanilla PINN with fewer training points and simpler architecture.