Transfer learning-enhanced physics-informed neural networks for photonic device simulation with mode modeling method
Year: 2026
Type: article
Abstract: Modal analysis techniques like eigenmode expansion and coupled mode theory are crucial for device simulation in photonic integrated circuits. Conventional numerical methods are inefficient for finely discretized graded-index structures, as they require repeated eigenmode computations across similar cross-sections. To overcome this, we propose a Transfer Learning–Enhanced Multi-Mode Physics-Informed Neural Network (TL-MmPINN) for efficient eigenmode searching in such structures. The framework starts with a single-mode PINN that jointly learns the eigenvalue-eigenfunction pair for a given waveguide configuration and is fine-tuned for similar structures. Building on this, a multi-branch architecture with orthogonality, nontriviality, and mode-selection constraints enables simultaneous prediction of multiple modes. A transfer-learning pipeline further allows pretrained models to be reused for perturbed waveguides, avoiding the repeated discretization and eigenvalue decomposition required by conventional solvers. Numerical experiments show that TL-MmPINN achieves accuracy comparable to finite-difference eigen-solvers while reducing per-structure training iterations, demonstrating its potential as a reusable and efficient tool for photonic device modeling.
Source: Optics Continuum
Authors: Yunhao Mi, Chonglei Sun, Liuge Du, Xiao Xu, Jia Zhao
Institution: ShanghaiTech University
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,372
Awards: G2283477455, G6001024484
OpenAlex ID: W7125425905