Mosallaei’s Research Featured on Cover of Advanced Theory and Simulations

cover of publication Advanced Theory and Simulations

ECE Professor Hossein Mosallaei’s research on “Machine Learning: TCO‐Based Active Dielectric Metasurfaces Design by Conditional Generative Adversarial Networks” was featured on the cover of Advanced Theory and Simulations. Hossein Mosallaei and co‐workers propose a machine learning (ML) method for broad‐band multi‐modal inverse design of TCO‐based active metasurfaces. Their proposed technique is a combination of a K‐means clustering algorithm and conditional generative adversarial networks (cGANs). It casts light on how ML models can solve inverse‐design problems and the level of intelligence they can provide.


While researchers in the field of active flat optics continue to make groundbreaking progress by seeking novel materials and control systems, the complexity and sensitivity of the nanostructures that they aspire to design are unavoidably increasing. Inverse design of the popular class of transparent conducting oxide (TCO)‐based active metasurfaces is particularly challenging, largely due to the limited choice of the active materials, and sensitive physical mechanisms that give rise to their tunability. In this contribution, a new machine learning method based on a combination of the K‐means clustering algorithm and conditional Wasserstein generative adversarial networks (cWGANs) for broadband multi‐modal inverse design of TCO‐based active metasurfaces is developed. Multi‐objective evolutionary optimization is adopted to efficiently generate a diverse training dataset of high‐performance active metasurfaces. The training dataset includes samples that operate at specific wavelengths throughout the optical telecommunications (telecom) band. K‐means algorithm is then used to extract the clusters (modes) present in the training dataset, and exclusive cWGAN models are fit on each of them. The model is capable of generating designs operating at wavelengths that are not present in the training dataset. It also provides a clear picture of the feasibility and interplay between the design objectives.

Related Faculty: Hossein Mosallaei

Related Departments:Electrical & Computer Engineering