Talk TitleThe Transformative Impact of Machine Learning Enabled Computational Electromagnetics on the Future of WirelessAbstractThe continuous proliferation of wireless technologies, from 5G communications to the Internet of Things, creates a compelling need to intelligently plan the deployment of such systems in indoor and outdoor environments. This planning is required to meet the desired Quality of Service objectives (e.g. high bit-rates for Wi-Fi networks) along with safety standards for exposure of users to radiated emissions, and to ensure compatibility with existing systems. Wireless propagation modeling, which is the prediction of the electromagnetic field levels generated by a wireless communication system, is an essential element of such an intelligent planning process. These models can be deduced by numerical algorithms based on the physics of electromagnetic wave propagation, or by <a href="http://measurements.Software-based" target="_blank" title="measurements.Software-based">measurements.Software-based planning is a reality in several areas, including the design of environmentally friendly buildings, where simulation tools are used to optimize heat and air flow. The question is how to enable a similar approach for wireless infrastructure that is becoming as indispensable as any other infrastructure <a href="http://element.This" target="_blank" title="element.This">element.This presentation is aimed at demonstrating that machine learning enabled propagation models can address this question, overcoming the dichotomy between accuracy and efficiency that has dominated this area for decades. We give an overview of the most recent advances in the field, including neural networks that can accurately predict, in real-time, signal strength levels of indoor and outdoor wireless networks by processing the geometry and the position of one or more transmitters. We discuss the use of such models for the rapid placement of massive numbers of access points of wireless networks, such as those providing wireless connectivity to spectators in large sports venues. Finally, we show that this research leads to reliable “digital twins” of wireless communication systems. These are robust computational models that allow for the full evaluation of the performance of wireless networks, under changes in the environment and the conditions of operation over <a href="http://time.Speaker" target="_blank" title="time.Speaker">time.Speaker BioProf. 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 <a href="http://learning.He" target="_blank" title="learning.He">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: Monday, September 30, 2025, at 11:00 AMLocation: University of Waterloo, EIT 3142Speaker: Prof. Costas Sarris, IEEE Fellow, University of TorontoCo-Organizers-IEEE KW AP-S Student Chapter-IEEE KW MTT-S Student Chapter-IEEE KW Joint AP-S and MTT-S Chapter-IEEE KW Sensors Council Chapter-IEEE KW Young ProfessionalsEIT 3142, 200 University Ave W, ON, Waterloo, Ontario, Canada, N2L 3G1, Virtual: https://events.vtools.ieee.org/m/501586