The Texas A&M Institute of Data Science (TAMIDS) invites you to attend the 2022 TAMIDS Data Science Webinar series on Apr 5th, 2022. This webinar series will introduce some intermediate and advanced topics of Data Science to Texas A&M students and researchers.
Presenter:
Dr. Ming Zhong is a postdoctoral researcher in the Scientific Machine Learning Lab (SciML Lab) at the Texas A&M Institute of Data Science (TAMIDS). He works with Prof. Ulisses Braga-Neto on various physics-informed machine learning methods for solving stiff and hyperbolic PDEs. Ming received his Ph.D. in Applied Mathematics, under the guidance of Dr. Eitan Tadmor from the Department of Mathematics at the University of Maryland.
Research Interests:
- Physics-Informed Machine Learning of Collective Behaviors from Observation
- Physics-Informed Machine Learning for solving various PDEs
- Numerical PDEs, especially central schemes for hyperbolic conservation laws
- Computational Optimal Transport Plan
- Geometric Numerical Integrators
- Scientific Computing (MPI, openMP, CUDA)
Time:
2:00 PM – 3:30 PM CDT, Apr 5th
Description:
Self Organization (clustering, flocking, swarming, etc.) is ubiquitous in many natural phenomena. We seek to understand the essence of such behaviors from the mathematical point of view. We provide various forward modeling methods for explaining clustering, flocking, swarming, and synchronization. Most importantly, we will discuss in detail our physics-informed data-driven approach to derive meaningful dynamical systems to describe self-organization from observation data.
Prerequisites:
Basic knowledge of linear algebra, calculus, modeling, Python (or MATLAB) is assumed.