Machine Learning for Accelerated and Inverse Metasurface Design

Hosted By: Photonic Metamaterials Technical Group

2 April 2020, 13:00 - 14:00

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In this webinar hosted by the OSA Photonic Metamaterials Technical Group, Dr. Willie Padilla of Duke University will provide an overview on the emergence of machine learning/deep learning applied to the study of metasurfaces, including inverse design.

Dr. Padilla’s talk will be motivated by illustrating the challenges and opportunities of all-dielectric metasurfaces contrasted to those based on metallic metasurfaces. Four different neural network architectures will be explored and the performance of each will be detailed, with results from the highest performing model shown explicitly. A solution to the inverse model will be presented during the webinar, which offers significant opportunities for design of advanced structured materials for challenging applications.

What You Will Learn:

  • Challenges and opportunities in all-dielectric metasurfaces
  • Overview of machine learning for metasurface design
  • Efficient inverse design solution

Who Should Attend:

  • Research scientists and engineers in university, government and industrial laboratories
  • Master and PhD students

About the Presenter: Willie Padilla, Duke University

Willie Padilla is a Full Professor in the Department of ECE at Duke University with MS and PhD degrees in Physics from the University of California San Diego. He was a Director’s Postdoctoral Fellow at Los Alamos National Laboratory. In 2007 he was awarded a Young Investigator Award from the Office of Naval Research, and Presidential Early Career Award for Scientists and Engineers in 2011. In 2012 he was elected a Fellow of the Optical Society of America, and a Kavli Frontiers of Science Fellow in 2013. Dr. Padilla was elevated to Senior Member of the SPIE in 2018, and is a Fellow of the American Physical Society. Professor Padilla has more than 200 peer-reviewed journal article, two book chapters and seven issued patents. He heads a group working in the area of artificially structured systems including metamaterials with a focus on machine learning, computational imaging, spectroscopy and energy.