Lexing Ying, Stanford University, USA
Conference ID: 413-639-086
PIN Code: 455031
This talk is about some recent progress on solving inverse problems using deep learning. Compared to traditional machine learning problems, inverse problems are often limited by the size of the training data set. We show how to overcome this issue by incorporating mathematical analysis and physics into the design of neural network architectures. We first describe neural network representations of pseudodifferential operators and Fourier integral operators. We then continue to discuss applications including electric impedance tomography, optical tomography, inverse acoustic/EM scattering, seismic imaging, and travel-time tomography.
Lexing Ying has been Professor of Mathematics at Stanford University since 2012. Prior to that, he was a professor at the University of Texas at Austin from 2006 to 2012. His research focuses on computational mathematics and scientific computing. He received his Ph.D. from New York University and was a postdoctoral scholar at California Institute of Technology from 2004 to 2006. He is a recipient of the Sloan Research Fellowship (2007), the National Science Foundation CAREER Award (2009), the Feng Kang Prize of Scientific Computing (2011), the James H. Wilkinson Prize in Numerical Analysis and Scientific Computing from SIAM (2013), and the Silver Morningside Medeal in Applied Mathematics (2016).