A Physics-Informed Neural Network-Based Waveguide Eigenanalysis

Abstract

This work presents a deep neural network (DNN)-based approach for identifying the modal field distributions of closed non-radiating waveguides. Specifically, physics-informed neural networks (PINNs) are used to solve the Helmholtz partial differential equation. The PINN architecture includes incorporation of boundary conditions and selection of initial conditions to obtain required modes inside the waveguides. In this paper, furthermore, the use of this method is illustrated for waveguides consisting of inhomogeneous and anisotropic media, where we apply a domain decomposition-based deep learning method. Our approach successfully identifies all eigenmode distributions with an error of less than −12 dB as compared to analytical and full-wave simulation results. Notably, we further enhance the efficiency of our approach by utilizing transfer learning, achieving a 23 times reduction in solution time. Our results demonstrate PINNs as an alternative to traditional methods in accurately calculating waveguide modal field distributions and its applicability to other partial differential equation based EM problems.

Keywords

• Physics-informed neural network
• deep learning
• domain decomposition
• transfer learning
• eigenanalysis
• waveguides