Model-based Deep Learning for High-Dimensional Periodic Structures
Lucas Polo-López Luc Le Magoarou Romain Contreres María García-Vigueras
This work presents a deep learning surrogate model for the fast simulation of
high-dimensional frequency selective surfaces. We consider unit-cells which are
built as multiple concatenated stacks of screens and their design requires the
control over many geometrical degrees of freedom. Thanks to the introduction of
physical insight into the model, it can produce accurate predictions of the
S-parameters of a certain structure after training with a reduced dataset.The
proposed model is highly versatile and it can be used with any kind of
frequency selective surface, based on either perforations or patches of any
arbitrary geometry. Numeric examples are presented here for the case of
frequency selective surfaces composed of screens with rectangular perforations,
showing an excellent agreement between the predicted performance and such
obtained with a full-wave simulator.