PIMS-CRG Seminar / UCM Scientific Computing and Data Science Seminar: Yingda Cheng
Topic
Two numerical algorithms for Tucker tensor
Speakers
Details
In this talk, we present two algorithms associated with Tucker tensor format. The first is to obtain the Tucker tensor based on a new cross approximation we developed, called Cross^2-DEIM. This method samples a few fibers (proportionate to the multi-linear rank) in each mode in a FSTD2 fashion. We use DEIM index selection for both the main and complement index sets. It is shown to be more efficient in computing the Tucker tensor format than existing cross approximations. The second method is a solver for nonlinear tensor equations in Tucker format by Anderson Acceleration (AA). This is an extension of our prior results of low rank AA. We show the the method works well for benchmark problems, such as Bratu problem and Allen-Cahn equations. This is joint work with Daniel Appelo (VT).
Additional Information
This is a hybrid event. Zoom Link: https://ucmerced.zoom.us/j/87172798382?pwd=j3isl8O1fT3cW5gKUBFvokYbGriv3t.1
Speaker Bio:
Yingda Cheng is a professor at the Department of Mathematics and an affiliated faculty with CMDA (Computational Modeling and Data Analytics) program, Virginia Tech. Her area of research is scientific computing, applied mathematics and data-driven modeling and computation. Specifically, she is interested in developing structure-preserving schemes for high dimensional PDEs. She received B.S. degree from University of Science and Technology of China in 2003 and Ph.D. degree in Applied Mathematics from Brown University in 2007. After a postdoctoral position at the University of Texas at Austin, she was a faculty member at Michigan State University (2011-2023) before she joined Virginia Tech in 2023. She is a recipient of the NSF Career award (2015), Simons Fellowship (2018) and SIAM Germund Dahlquist Prize in 2023.
