Dr. Costas Sarris: The transformative impact of machine learning enabled computational electromagnetics on the future of wireless
The 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 measurements. Numerical prediction methods require a high level of relevant expertise and significant computational resources. As a result, wireless service planning mostly relies on resource-consuming measurement campaigns. 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 element.
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- 856 Campus Pl NW
- Calgary, Alberta
- Canada T2N4V8
- Building: ICT
- Room Number: 516
- Starts 29 May 2024 07:56 PM UTC
- Ends 25 June 2024 07:00 PM UTC
- No Admission Charge
Speakers
Dr. Sarris of University of Toronto
The transformative impact of machine learning enabled computational electromagnetics on the future of wireless
Biography:
Dr. Costas Sarris is a Professor with the Department of Electrical and Computer Engineering at University of Toronto, Toronto, Ontario. His research area is computational electromagnetics, with an emphasis on time-domain modeling, adaptive mesh refinement, enhanced stability, and higher order methods. He also works on physics-based wireless propagation models (with full-wave, asymptotic, and hybrid techniques), uncertainty quantification, and scientific machine learning. Dr. Sarris was a recipient of the 2021 Premium Award for Best Paper in IET Microwaves, Antennas & Propagation, the IEEE MTT-S Outstanding Young Engineer Award in 2013 and an Early Researcher Award from the Ontario Government in 2007. He was the TPC Chair of the 2015 IEEE AP-S International Symposium on Antennas and Propagation and the CNC/USNC Joint Meeting, the 2019 and 2023 MTT-S Numerical Electromagnetics, Multiphysics and Optimization (NEMO) Conference, the TPC Vice-Chair of the 2012 IEEE MTT-S International Microwave Symposium, and the Chair of the MTT-S Technical Committee on Field Theory and Numerical Electromagnetics (2018–2020). Since 2019, he has been serving as the Editor-in-Chief of the IEEE JOURNAL ON MULTISCALE AND MULTIPHYSICS COMPUTATIONAL TECHNIQUES. He was a Guest Editor of the IEEE Microwave Magazine’s Special Issue on machine learning for microwave engineering (October 2021), and an Associate Editor of the IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES (2009–2013) and the IEEE MICROWAVE AND WIRELESS COMPONENTS LETTERS (2007–2009).