Dr. Shuai Yan
Shuai Yan is currently an associate researcher in Institute of Electrical Engineering, Chinese Academy of Sciences. She received B.S. and M.S. degree in Mathematics from Beijing Normal University, China in 2007 and 2010 respectively. In 2014, She received her Ph.D. degree in Computational Engineering in Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Germany. She has authored over 30 peer-reviewed papers in international journals, and serves as a reviewer for several academic journals in the computational electromagnetics community. She also serves as a key developper for the software platform for electromagnetic and multi-physics coupling analysis, EMPbridge.
The main research interests are: Algorithms and software in computational electromagnetics; Model order reduction; Artificial intelligence in scientific computing.
Presentation: Efficient and high-fidelity physical state evaluation: a survey of meta-modeling techniques for digital twins
Real-time monitoring and predictive analysis of physical systems are essential for the realization of digital twins, yet achieving a balance between computational efficiency and model fidelity remains a critical challenge. In this presentation, drawing on case studies from our research group, we review state-of-the-art meta-modeling frameworks tailored for efficient and high-fidelity physical state evaluation, including model order reduction and different kinds of machine learning frameworks. The pros and cons of different frameworks will be discussed. Furthermore, open challenges will be analyzed and future research directions will be outlined.