Scientific Machine Learning for Computational electromagnetics: from Microwave Circuits to Radiowave Propagation
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”.
We explore the potential of scientific machine learning methods to problems in computational electromagnetics starting
from standard microwave structure design and multiphysics modeling, 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 ground truth data from a large number of simulations (as in typical neural networks).
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. We show 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. These models can be used to rapidly optimize the position of transmitters in wireless area networks, to maximize
coverage or other relevant metrics.
Date and Time
Location
Hosts
Registration
- Date: 07 Jul 2023
- Time: 03:00 PM to 04:30 PM
- All times are (UTC-04:00) Eastern Time (US & Canada)
-
Add Event to Calendar
- McGill Unversity
- Montreal, Quebec
- Canada
- Building: McConnell Eng. Building
- Room Number: Room 603
- Contact Event Host
- Co-sponsored by STARaCom
- Starts 10 June 2023 02:20 PM
- Ends 07 July 2023 04:00 PM
- All times are (UTC-04:00) Eastern Time (US & Canada)
- No Admission Charge
Speakers
Costas Sarris of University of Toronto
Scientific Machine Learning for Computational electromagnetics: from Microwave Circuits to Radiowave Propagation
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”.
We explore the potential of scientific machine learning methods to problems in computational electromagnetics starting
from standard microwave structure design and multiphysics modeling, 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 ground truth data from a large number of simulations (as in typical neural networks).
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. We show 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. These models can be used to rapidly optimize the position of transmitters in wireless area networks, to maximize
coverage or other relevant metrics.
Biography:
Costas Sarris received the Ph.D. degree in electrical engineering and the M.Sc. degree in applied mathematics from the University of Michigan, Ann Arbor, MI, USA, both in 2002.
He is currently a Full Professor with the Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada. 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 IEEE MTT-S Outstanding Young Engineer
Award in 2013 and an Early Researcher Award from the Ontario Government in 2007. He is the Editor-in-Chief of the IEEE JOURNAL ON MULTISCALE AND MULTIPHYSICS COMPUTATIONAL TECHNIQUES.