Improving Generative Inverse Design of Rectangular Patch Antennas with Test Time Optimization
This work addresses the problem of efficient antenna design for engineers, though it appears incremental as it builds on existing generative and optimization methods.
The authors tackled the inverse design of rectangular patch antennas by proposing a two-stage deep learning framework that learns latent representations of frequency responses and generates feasible geometries, with test-time optimization improving design accuracy and enabling consideration of manufacturability.
We propose a two-stage deep learning framework for the inverse design of rectangular patch antennas. Our approach leverages generative modeling to learn a latent representation of antenna frequency response curves and conditions a subsequent generative model on these responses to produce feasible antenna geometries. We further demonstrate that leveraging search and optimization techniques at test-time improves the accuracy of the generated designs and enables consideration of auxiliary objectives such as manufacturability. Our approach generalizes naturally to different design criteria, and can be easily adapted to more complex geometric design spaces.