A Probabilistic Machine Learning Approach for the Uncertainty Quantification of Electronic Circuits Based on Gaussian Process Regression-Reference-Cited by-同舟云学术

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A Probabilistic Machine Learning Approach for the Uncertainty Quantification of Electronic Circuits Based on Gaussian Process Regression

Published:2021 Issue: Volume: Page:1-1
ISSN:0278-0070
Container-title:IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
language:
Short-container-title:IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

Author:

Manfredi Paolo,Trinchero Riccardo

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Graphics and Computer-Aided Design,Software

Link

http://xplorestaging.ieee.org/ielx7/43/6917053/09536589.pdf?arnumber=9536589

Cited by 16 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Wideband Kriging for Multiobjective Optimization of a High-Voltage EMI Filter;IEEE Transactions on Electromagnetic Compatibility;2024-08

2. Conservative Gaussian Process Models for Uncertainty Quantification and Bayesian Optimization in Signal Integrity Applications;IEEE Transactions on Components, Packaging and Manufacturing Technology;2024-07

3. Uncertainty Quantification and Sensitivity Analysis of Crosstalk in PCB Lines via the Combination of Machine Learning and Polynomial Chaos Expansion;2024 IEEE Joint International Symposium on Electromagnetic Compatibility, Signal & Power Integrity: EMC Japan / Asia-Pacific International Symposium on Electromagnetic Compatibility (EMC Japan/APEMC Okinawa);2024-05-20

4. Uncertainty Quantification and Optimal Design of EV-WPT System Efficiency based on Adaptive Gaussian Process Regression;The Applied Computational Electromagnetics Society Journal (ACES);2024-04-16

5. Kriging Methodology for Uncertainty Quantification in Computational Electromagnetics;IEEE Open Journal of Antennas and Propagation;2024-04

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