Events
Department of Mathematics and Statistics
Texas Tech University
abstract pdf
 | Wednesday
Feb. 4
|
| Algebra and Number Theory
No Seminar
|
Abstract: Operator learning has undergone rapid development in recent years and gradually emerged as a transformative machine-learning paradigm. In this talk, I will provide an overview of the operator-learning framework and explore its applications to computational problems, with a particular focus on quantum many-body dynamics. We will discuss how traditional machine learning models, such as recurrent neural networks (RNNs), can be adapted to learn operators and predict the nonequilibrium dynamics of quantum many-body systems. Additionally, I will highlight how transformer-based neural operators can be employed to model the self-energy of strongly correlated systems. Through these examples, we aim to showcase the potential of operator learning to tackle key challenges in quantum many-body systems, especially when the dynamics are high-dimensional but not necessarily chaotic.
When: 4:00 pm (Lubbock's local time is GMT -6)
Where: room Math 011 (Math Basement)
ZOOM details:
- Choice #1: use this Direct Link that embeds meeting and ID and passcode.
- Choice #2: Login to zoom, then you will have to input the ID and Passcode by hand to join:
* Meeting ID: 949 9288 2213
* Passcode: Applied
Abstract pdf
The Biomath seminar may be attended virtually Friday at 11:00 AM CST (UTC-6) via this Zoom link.
Meeting ID: 938 8653 3169
Passcode: 883472
abstract 2 PM CST (UTC-6)
Zoom link available from Dr. Brent Lindquist upon request.