Title: Algorithm-driven paradigms for freeform photonic metamaterials
Abstract: In this talk, I will discuss advances in photonic engineering in which algorithmic approaches to device implementation unlock new functional capabilities. In the first part, I will discuss the utilization of freeform optimization for metamaterials in which non-local, multiple scattering dynamics enable new regimes of device efficiency and multi-functionality. These concepts are versatile and I will discuss various device demonstrations including geometric phase metasurface systems supporting broadband circular birefringence control and conformal volumetric metamaterials capable of highly multiplexed waveform control. The second part will focus on innovations in hybrid data-physics neural networks, where I will show how deep generative networks can be used to perform population-based global optimization and how physics-augmented deep networks can serve as accurate surrogate electromagnetic solvers. We anticipate that the ability for deep learning models to dramatically accelerate and even automate the simulation and design of photonic systems will push the innovation cycle in all domains of photonics research.
About the Speaker: Dr. Jonathan Fan is an Associate Professor in the Department of Electrical Engineering at Stanford University, where he is researching topics at the intersection of algorithms, materials science, and photonics. He received his bachelor’s degree with highest honors from Princeton University and his doctorate from Harvard University. He is the recipient of the Air Force Young Investigator Award, Sloan Foundation Fellowship in Physics, Packard Foundation Fellowship, and the Presidential Early Career Award for Scientists and Engineers.
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