Please join us for the September talk of the MIT.nano Seminar Series:
Jonathan Fan
Assistant Professor of Electrical Engineering
Stanford University
Date: Monday, September 14, 2020
Time: 3pm - 4pm EST
Location: Zoom webinar
After registering, you will receive the link to join. This event is free and open to the public.
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Abstract
In this talk, Fan will present new algorithmic approaches to the inverse design of freeform electromagnetic devices. His focus will be on an optimization strategy based on physics-driven neural networks, termed GLOnets, in which the global optimization process is reframed as the training of a generative neural network. He will discuss how this method incorporates physics and physical constraints through the interfacing of Maxwell’s equations with machine learning, and he will frame the discussion around examples of metasurfaces and thin film stacks operating near physical design limits. These ideas will help set the stage for hybrid physics- and data-driven approaches to be used in defining the next frontier of electromagnetics engineering.
Biography
Jonathan Fan is an Assistant Professor in the Department of Electrical Engineering at Stanford University, where he is researching new design methodologies and materials approaches to nanophotonic systems. 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.