Industry experts and academic professors from Carleton University, University of Toronto, and Ericsson will be hosting an AI/ML boot camp at this year’s IEEE International Microwave Symposium (IMS), which will be held in Washington D.C from June 16-21, 2024. This boot camp is intended for engineers who want to learn the basics of AI/ML or are interested in using AI/ML for microwave applications, marketing and sales professionals who are interested in understanding the basics and relevance of AI/ML for microwaves, and university students who like to acquire the basic knowledge of AI/ML.
The AI/ML Boot Camp will present the basics of Artificial Intelligence (AI)/machine learning (ML) for microwaves. The course is targeted to general audiences in the microwave community who are not necessarily experts in AI/ML. To start with, the course addresses basic questions such as: what is AI/ML. Why are AI/ML tools relevant for microwave community. How can AI/ML be used in microwave applications, and how can it be adopted in microwave circuits and systems. We also address what the benefits and limitations of using AI/ML in microwave technologies are. The bootcamp will introduce basic types of machine learning methods such as multilayer perceptrons, radial basis function networks, convolutional neural networks, time-delay neural networks, recurrent neural networks, long-short term memory networks, generative adversarial networks, and reinforcement learning. Examples of applications of AI/ML to microwaves will be presented.
Organizers: Qi-Jun Zhang, Costas Sarris, Ulf Gustavsson
Organizer organizations: Carleton University, University of Toronto, Ericsson
Location: 206
Key Presentations to Take Place at the AI/ML Boot Camp
AIB1-1: AI and Machine Learning for Microwave Design - An Introduction
Author: Qi-Jun Zhang
Organization: Carleton University
Introductory presentation covering fundamental AI/ML concepts.
AIB1-2: Scientific Machine Learning: Principles, Methods and Applications
Author: Costas Sarris
Organization: University of Toronto
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).
AIB1-3: AI for 3D Radar – Approaches and Opportunities
Author: Asaf Tzadok
Organization: IBM T.J. Watson Research Center
AIB1-4: Augmented Intelligence for End-to-End Design
Author: Xia (Ivy) Zhu
Organization: Intel Corporation
At the end, the team will also provide ample opportunities for audience interaction and Q&A.
Click here to learn more about AI/ML Boot Camp.
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