Optical Scattering Characteristics of Three-Dimensional Targets Based on Transfer Learning PINNs

Physics-Informed Neural Networks (PINNs) continue to suffer from limited training efficiency in predicting electromagnetic scattering from three-dimensional (3D) targets, resulting in particularly high retraining costs when target geometries or incident fields vary. To address this issue, we propose a transferable PINN framework constrained by discrete Maxwell operators. By embedding finite-difference electromagnetic operators into the loss function, the predicted fields directly satisfy the discrete Maxwell's equations on the computational grid, thereby enhancing physical consistency and improving training stability. Furthermore, a layer-freezing strategy is employed to preserve the generalized wave priors learned from the source scenario. Consequently, training under novel geometries or incident conditions no longer relies on full-network retraining; instead, rapid prediction is achieved by fine-tuning a minimal set of task-specific parameters. Numerical results demonstrate that the proposed method significantly reduces the number of iterations and retraining costs while maintaining prediction accuracy, making it an effective tool for rapid electromagnetic simulations and optical device design.