Element-wise Multiplication Based Deeper Physics-Informed Neural Networks
This work addresses limitations in PINNs for industrial and scientific applications, but appears incremental as it builds on existing PINN frameworks with a specific architectural modification.
The paper tackled the issues of lack of expressive ability and initialization pathology in Physics-Informed Neural Networks (PINNs) for solving partial differential equations, proposing a Deeper-PINN using element-wise multiplication to enhance performance, with results showing effective resolution of these problems.
As a promising framework for resolving partial differential equations (PDEs), Physics-Informed Neural Networks (PINNs) have received widespread attention from industrial and scientific fields. However, lack of expressive ability and initialization pathology issues are found to prevent the application of PINNs in complex PDEs. In this work, we propose Deeper Physics-Informed Neural Network (Deeper-PINN) to resolve these issues. The element-wise multiplication operation is adopted to transform features into high-dimensional, non-linear spaces. Benefiting from element-wise multiplication operation, Deeper-PINNs can alleviate the initialization pathologies of PINNs and enhance the expressive capability of PINNs. The proposed structure is verified on various benchmarks. The results show that Deeper-PINNs can effectively resolve the initialization pathology and exhibit strong expressive ability.