Bayesian Learning for Uncertainty Quantification, Optimization, and Inverse Design
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...
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| Veröffentlicht in: | IEEE transactions on microwave theory and techniques 2022-11, Vol.70 (11), p.4620-4634 |
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| container_title | IEEE transactions on microwave theory and techniques |
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| creator | Swaminathan, Madhavan Bhatti, Osama Waqar Guo, Yiliang Huang, Eric Akinwande, Oluwaseyi |
| description | 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. |
| doi_str_mv | 10.1109/TMTT.2022.3206455 |
| format | Article |
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| subjects | Artificial neural networks Bayes methods Bayes Theorem Circuit design Complexity Computing time Design optimization Gaussian processes (GPs) Inverse design invertible neural networks Kernel Machine learning Microwave circuits Microwave theory and techniques Neural networks neural networks (NNs) Optimization Predictions Uncertainty |
| title | Bayesian Learning for Uncertainty Quantification, Optimization, and Inverse Design |
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