Distinguished Lecturer Talk by Distinguished Lecturer and IEEE Fellow Professor Costas Sarris
Talk Title: Scientific Machine Learning for Electromagnetic Field Computations
Abstract
A recent report by the US Department of Energy defines the area of scientific machine learning as “a core component of artificial intelligence (AI) and a computational technology that can be trained, with scientific data, to augment or automate human skills”, which has “the potential to transform science and energy research”. In this presentation, we discuss the potential of scientific machine learning methods to problems in computational electromagnetics starting from standard electromagnetic structure analysis and multi-physics modeling in the time-domain, employing an unsupervised learning strategy based on Physics-Informed Neural Networks (PINN). PINNs directly integrate physical laws into their loss function, so that the training process does not rely on the generation of training ground truth data from simulations (as in typical neural networks). We demonstrate the unconditionally stable solution of coupled electromagnetic-thermal problems, along with the modeling of frequency selective surfaces and metasurfaces, orders of magnitude faster than the conventional finite-difference time-domain technique (FDTD), including training time of the neural network.
Moreover, we demonstrate the impact of machine learning on the computational modeling of radiowave propagation scenarios. We build convolutional neural network models that can process the geometry of indoor environments, along with physics-inspired parameters, to rapidly estimate received signal strength (RSS) maps. Emphasis is placed on the generalizability of these models, which is their ability to "learn" the physics of radiowave propagation and produce accurate modeling predictions in new geometries well beyond those included in their training set.
Speaker Bio
Prof. Costas Sarris is a Professor of Electrical and Computer Engineering at the University of Toronto. His research spans computational electromagnetics, time-domain modeling, wireless propagation models, uncertainty quantification, and scientific machine learning.
He is an IEEE Fellow and a Distinguished Lecturer of the IEEE Antennas and Propagation Society (2024–2026). His many honors include the 2021 IET Premium Award for Best Paper in Microwaves, Antennas & Propagation and the 2013 IEEE MTT-S Outstanding Young Engineer Award. He has served in numerous leadership roles, including Editor-in-Chief of the IEEE Journal on Multiscale and Multiphysics Computational Techniques (2019–2024).
Date & Time: Tuesday, September 30, 2025, at 11:00 AM
Location: University of Waterloo, EIT 3142
Speaker: Prof. Costas Sarris, IEEE Fellow, University of Toronto
Organizers
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IEEE KW MTT-S Student Branch Chapter
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IEEE KW AP-S Student Branch Chapter
Co-Organizers
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IEEE KW Joint AP-S & MTT-S Chapter
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IEEE KW Sensors Council Chapter
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IEEE KW Young Professionals
Date and Time
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- EIT 3142, 200 University Ave W, ON
- Waterloo, Ontario
- Canada N2L 3G1
- Starts 17 September 2025 07:00 PM UTC
- Ends 30 September 2025 04:00 PM UTC
- No Admission Charge