Speaker
Description
Recent advancements in generative artificial intelligence (AI), including transformers, adversarial networks, and diffusion models, have demonstrated significant potential across various fields, from creative art to drug discovery. Leveraging these models in engineering applications, particularly in nanophotonics, is an emerging frontier. Nanophotonic metasurfaces, which manipulate light at the subwavelength scale, require highly optimized meta-atom designs. Traditionally, optimizing such designs relied on computationally expensive, gradient-based methods, navigating exponentially large design spaces. In this work, we propose a novel machine learning-driven latent optimization approach, which improves the surrogate function correlation of latent optimization methods by enforcing a Pearson correlation through the usage of PearNets. Utilizing variational neural annealing, this technique effectively samples design candidates, achieving thermophotovoltaic efficiencies of up to 96.7%. Our method presents a scalable alternative for the design and optimization of nanophotonic devices, offering both reduction in computational complexity and improvements in accuracy in topological optimization.
