Bayesian Learning for Uncertainty Quantification, Optimization, and Inverse Design

Madhavan Swaminathan; Osama Waqar Bhatti; Yiliang Guo; Eric Huang; Oluwaseyi Akinwande

doi:10.1109/TMTT.2022.3206455

Bayesian Learning for Uncertainty Quantification, Optimization, and Inverse Design

Madhavan Swaminathan, Osama Waqar Bhatti, Yiliang Guo, Eric Huang, Oluwaseyi Akinwande

Electrical Engineering

Research output: Contribution to journal › Article › peer-review

7 Citations (SciVal)

Abstract

Design of microwave circuits require extensive simulations, which often take significant computational time due to design complexity. This can be addressed through neural networks (NNs) that provide predictive capability. Predictions often come with uncertainties that need to be quantified. Moreover, optimization and inverse designs are better done using probabilities. This article describes the use of Bayes theorem and machine learning (ML) for solving complex microwave design problems.

Original language	English (US)
Pages (from-to)	4620-4634
Number of pages	15
Journal	IEEE Transactions on Microwave Theory and Techniques
Volume	70
Issue number	11
DOIs	https://doi.org/10.1109/TMTT.2022.3206455
State	Published - Nov 1 2022

All Science Journal Classification (ASJC) codes

Radiation
Condensed Matter Physics
Electrical and Electronic Engineering

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10.1109/TMTT.2022.3206455

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abstract = "Design of microwave circuits require extensive simulations, which often take significant computational time due to design complexity. This can be addressed through neural networks (NNs) that provide predictive capability. Predictions often come with uncertainties that need to be quantified. Moreover, optimization and inverse designs are better done using probabilities. This article describes the use of Bayes theorem and machine learning (ML) for solving complex microwave design problems.",

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Bayesian Learning for Uncertainty Quantification, Optimization, and Inverse Design

Abstract

All Science Journal Classification (ASJC) codes

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