Lie Point Symmetry and Physics Informed Networks
This work addresses the challenge of enhancing neural solvers for PDEs, which is incremental as it builds on existing PINN methods by incorporating symmetry principles.
The authors tackled the problem of improving physics-informed neural networks (PINNs) for solving partial differential equations (PDEs) by integrating Lie point symmetries into the loss function, resulting in a significant boost in sample efficiency as indicated by empirical evaluations.
Symmetries have been leveraged to improve the generalization of neural networks through different mechanisms from data augmentation to equivariant architectures. However, despite their potential, their integration into neural solvers for partial differential equations (PDEs) remains largely unexplored. We explore the integration of PDE symmetries, known as Lie point symmetries, in a major family of neural solvers known as physics-informed neural networks (PINNs). We propose a loss function that informs the network about Lie point symmetries in the same way that PINN models try to enforce the underlying PDE through a loss function. Intuitively, our symmetry loss ensures that the infinitesimal generators of the Lie group conserve the PDE solutions. Effectively, this means that once the network learns a solution, it also learns the neighbouring solutions generated by Lie point symmetries. Empirical evaluations indicate that the inductive bias introduced by the Lie point symmetries of the PDEs greatly boosts the sample efficiency of PINNs.