Periodic Structure

Periodic structures are regularly repeating patterns found across numerous scientific domains, and research focuses on efficiently modeling, generating, and analyzing these structures. Current efforts leverage deep learning, employing architectures like variational autoencoders and diffusion models, to predict material properties, design novel materials with specific symmetries, and classify wave behavior within these structures. These advancements improve the speed and accuracy of simulations and design processes, impacting fields ranging from materials science (e.g., designing novel polymers) to photonics (e.g., optimizing frequency selective surfaces). The development of efficient, parameter-reduced neural networks further enhances the applicability of these methods.