In recent years, research in artificial intelligence techniques comprising both optimization and machine learning methodologies has attracted much attention. In particular, with the help of big data technology, massively parallel computing, and fast optimization algorithms, deep learning has greatly improved the performance of many problems in speech and image research. In this short tutorial, the presenters will share some of their learnings in artificial intelligence and deep learning techniques and discuss the potential and feasibility of applying them in computational electromagnetics. After discussing the fundamentals of such paradigms, the presenters will explore the characteristics, feasibility, and challenges of these methods in computational electromagnetics through examples of solving electromagnetic forward modeling and inverse problems.
Speakers
Maokun Li, Tsinghua University
Marco Salucci, University of Trento
When: Sunday, 13 July 2025, 09:00 – 12:00