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

19 Scopus citations

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

Access to Document

10.1109/TMTT.2022.3206455

Cite this

@article{965de22398964b9e94a50a7a7e75cda9,

title = "Bayesian Learning for Uncertainty Quantification, Optimization, and Inverse Design",

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.",

author = "Madhavan Swaminathan and Bhatti, \{Osama Waqar\} and Yiliang Guo and Eric Huang and Oluwaseyi Akinwande",

note = "Publisher Copyright: {\textcopyright} 1963-2012 IEEE.",

year = "2022",

month = nov,

day = "1",

doi = "10.1109/TMTT.2022.3206455",

language = "English (US)",

volume = "70",

pages = "4620--4634",

journal = "IEEE Transactions on Microwave Theory and Techniques",

issn = "0018-9480",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

number = "11",

}

TY - JOUR

T1 - Bayesian Learning for Uncertainty Quantification, Optimization, and Inverse Design

AU - Swaminathan, Madhavan

AU - Bhatti, Osama Waqar

AU - Guo, Yiliang

AU - Huang, Eric

AU - Akinwande, Oluwaseyi

PY - 2022/11/1

Y1 - 2022/11/1

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/85139823497

UR - https://www.scopus.com/pages/publications/85139823497#tab=citedBy

U2 - 10.1109/TMTT.2022.3206455

DO - 10.1109/TMTT.2022.3206455

M3 - Article

AN - SCOPUS:85139823497

SN - 0018-9480

VL - 70

SP - 4620

EP - 4634

JO - IEEE Transactions on Microwave Theory and Techniques

JF - IEEE Transactions on Microwave Theory and Techniques

IS - 11

ER -

Bayesian Learning for Uncertainty Quantification, Optimization, and Inverse Design

Abstract

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this