In this talk, I will introduce our recent progress on how deep learning models inspired us in solving real world problems with different applications. In particular, I will focus on deterministic and probabilistic deep learning models for inverse design of broadband acoustic cloak, which can conceal an object from incident acoustic waves over a broad frequency range; and a deep learning framework based on fully connected neural network to design plasmonic metascreen for efficient light trapping in ultrathin silicon solar cells.
Dr. Ying Wu is an associate professor in Applied Mathematics and Computational Sciences with secondary affiliations with the Electrical and Computer Engineering and Applied Physics programs. She received her BSc from Nanjing University in 2002 and PhD from the Hong Kong University of Science and Technology (HKUST) in 2008. Her research focuses on the development of innovative models and computational tools to describe wave propagation in complex systems. She serves as a co-editor for EPL and an associate editor for Wave Motion. She was awarded the Young Investigator Award by the International Phononics Society in 2017.