A PINN framework for inverse physical design of metal-loaded electromagnetic devices

To solve the difficulty of inverse design of metal-loaded electromagnetic devices, we propose a physics-informed neural network (PINN) framework. With the emergence of PINNs, some scholars within the field of electromagnetism have utilized them to design dielectric-loaded electromagnetic devices. However, using PINN to design metal-loaded electromagnetic devices is still uncommon due to the difficulty of binarization of metal shapes. In light of this, we propose a PINN framework to address the inverse physical design of metal-loaded electromagnetic devices. In PINN, we embed mathematical-physics models into the network, which endows the training parameters of PINN with practical physical significance. The main innovation of this paper is to solve the binarization problem in the inverse design of metal-loaded devices within the PINN. To verify the feasibility of our method, we provide an example of designing a dual-layer metal diaphragm-loaded parallel plate waveguide device. The PINN presented with metal binarization in this paper offers a novel approach to the inverse physical design of metal-loaded electromagnetic devices.