Bayesian Learning for Uncertainty Quantification, Optimization, and Inverse Design

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

Research output: Contribution to journalArticlepeer-review

4 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 languageEnglish (US)
Pages (from-to)4620-4634
Number of pages15
JournalIEEE Transactions on Microwave Theory and Techniques
Volume70
Issue number11
DOIs
StatePublished - Nov 1 2022

All Science Journal Classification (ASJC) codes

  • Radiation
  • Condensed Matter Physics
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Bayesian Learning for Uncertainty Quantification, Optimization, and Inverse Design'. Together they form a unique fingerprint.

Cite this