  This paper uses Physics-Informed Neural Network (PINN) to design Frequency
Selective Surface (FSS). PINN integrates physical information into the loss
function, so training PINN does not require a dataset, which will be faster
than traditional neural networks for inverse design. The specific
implementation process of this paper is to construct a PINN using field
solutions of mode matching method, and given the design goal, the PINN can
train the shape of the diaphragms. The single frequency FSS that meets the
design goal was designed using the inverse design method proposed in this paper
without a dataset, verifying the rationality of using PINN to design
metasurface. Using PINN for inverse design is not limited to single frequency
FSS, but can also be used for more complex metasurface.
