Physics-embedded Deep Learning for Electromagnetic Data Inversion
Recent research in deep learning techniques has attracted much attention. They
have also been applied to electromagnetic engineering. Data-driven approaches
allow machines to “learn” from a large amount of data and “master” the physical
laws in certain controlled boundary conditions. However, this technique also
faces many challenges, such as inaccuracy, limited generalization ability, etc.
In electromagnetic engineering, physical laws, i.e., Maxwell’s equations, set
major guidelines in research and development. They discover the nature of
electromagnetic fields and waves and are universal across various scenarios.
Incorporating physical principles into the deep learning framework significantly
improves deep neural networks' learning capacity and generalization ability,
hence increasing the accuracy and reliability of deep learning techniques in
modeling electromagnetic phenomena.
In this talk, we will study several techniques to embed physical simulation into
deep learning to model electromagnetic wave propagation. With the help of both
physical simulation and deep learning, we can improve the accuracy and computational
efficiency of electromagnetic modeling and data inversion. Hybridizing fundamental
physical principles with “knowledge” from big data could help electromagnetic
technologies be more automatic, accurate, and reliable.
Date and Time
Location
Hosts
Registration
- Date: 16 Apr 2025
- Time: 09:00 AM UTC to 10:00 AM UTC
-
Add Event to Calendar
- Via Mesiano 77
- Trento, Trentino-Alto Adige
- Italy
- Building: DICAM
- Room Number: 2R
Speakers
Prof. Maokun LI
Biography:
Maokun Li received a B.S. degree in electronic engineering from Tsinghua University,
Beijing, China, in 2002 and an M.S. and Ph.D. degrees in electrical engineering from
the University of Illinois at Urbana-Champaign, Champaign, IL, USA, in 2004 and 2007,
respectively. He then worked as a senior research scientist with Schlumberger-Doll
Research in Cambridge, MA, USA. In 2014, he joined the Department of Electronic
Engineering at Tsinghua University, Beijing. He is currently a professor at the Microwave
and Antenna Institute.
His research interest is in electromagnetic theory and computational electromagnetics,
especially in fast and reliable modeling and inversion algorithms for EM wave propagation
in complex environments, with applications to geophysical exploration, biomedical
imaging, etc. He is an associate editor of IEEE Transactions on Antennas and Propagation,
IEEE Transactions on Geoscience and Remote Sensing, and IEEE Journal on Multiscale and
Multiphysics Computational Techniques. He is also a member of the AP-S membership and
benefits committee and serves as the IEEE AP-S Distinguished Lecturer (2023-2025).
He is a Fellow of IEEE and ACES.