Special Session I: Applications of Deep Learning in Science and Engineering

In recent years, deep learning has found widespread applications in computer vision, speech recognition, and natural language processing. To advance research into the application of deep learning in natural sciences such as mathematics, physics, chemistry, and biology, this special topic will focus on applications of deep learning in mathematics, physics, and engineering disciplines, with particular emphasis on solving differential equations, Physics-Informed Neural Networks, channel modeling for communication systems, among others.

Related topics for this special session (but not limited to) :

• Solving differential equations using deep learning
• Physics-Informed Neural Networks
• Channel modeling for communication systems using deep learning

Short Biography of Organizer

Chun Jiang received the B.S. degree from Dalian Polytechnical University, Dalian, China, in 1989, and the M.S. and Ph.D. degrees in optical material and device from the Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai, China, in 1996 and 1999, respectively. From 1999 to 2001, he was a Postdoctoral Research Fellow with the State Key Laboratory of Advanced Optical Communication Systems and Networks, Shanghai Jiao Tong University, Shanghai, where he was an Associate Professor with the Department of Electronic Engineering from 2001 to 2004, and has been a Professor since 2004. He was engaged in the field of photonic device, wavelength-division multiplexing (WDM) transmission systems, and automatically switched optical networks. His current research interests include nanophotonics, photonic crystal device, and optical signal processing. He is the author or coauthor of more than 100 journal articles. He is the holder of ten issued Chinese patents.(Based on document published on 4 September 2020).