The U of T Student Chapter of the IEEE Antennas and Propagation Society (AP-S) invites you to the following talk of our 2020-2021 seminar series:

“Inverse Electromagnetics Design with Physics-Driven Neural Networks,”presented by Jonathan A. Fan from Stanford University, on Monday, Jan. 11, 2021, 4-5 PM ET. 

Day & Time: Monday, January 11, 2021
4:00 p.m. – 5:00 p.m.

Organizer: U of T Student Chapter of the IEEE Antennas and Propagation Society (AP-S)

Location: Online (link will be provided to registrants)

Contact: Parinaz Naseri

Abstract: In this talk, Prof. 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. Prof. Fan 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.