Microwave Engineering Applications of Machine Learning: Past, Present and Future

WORKSHOPS

Title: Microwave Engineering Applications of Machine Learning: Past, Present and Future

Place: International Microwave Symposium, Boston, MA

Date: 2019-06-07

Description: The remarkable advances in the available computational power over the past few years, and those anticipated to come, have propelled machine learning algorithms (some developed decades ago) to the forefront of R&D in a wide and diverse range of fields: from medicine to autonomous vehicles and robotics. As the interest in thee algorithms deepens, new algorithmic and theoretical developments are reported and applications are explored. These are assisted by the availability of open-source software tools and libraries, such as Google’s TensorFlow and PyTorch.

This workshop is a first step towards exploring the relevance and importance of machine learning for microwave engineers, and their CAD tools as used in industry and academia. We are combining a review of the field, its rich past in the microwave community (where artificial neural networks -ANNs- have been used as tools for microwave device modeling for many years) and its prospects, as developments in “deep learning” push the envelope of traditional ANNs even further, creating new opportunities to be harnessed.

SCHEDULE

1. Artificial Neural Networks for Microwave Design: An Overview
Abstract
Presenter: Qijun Zhang, Carleton Univ.
2.Bayesian Framework for Optimization and Generalization Techniques for Microwave Design
Abstract
Presenter: Madhavan Swaminathan, Georgia Institute of Technology
3. Time-Domain Computational Electromagnetics with Machine Intelligence
Abstract
Presenter: Zhen Peng, Univ. of New Mexico
4. Artificial Neural Networks for RF Component Modeling
Abstract
Presenter: Lei Zhang, NXP Semiconductors
5. Machine Intelligence for Electromagnetic Computations
Abstract
Presenter: Costas Sarris, Univ. of Toronto