Prediction on the relative permittivity of energy storage composite dielectrics using convolutional neural networks: A fast and accurate alternative to finite-element method

Prediction on the relative permittivity of energy storage composite dielectrics using convolutional neural networks: A fast and accurate alternative to finite-element method

The relative permittivity is one of the essential parameters determines the physical polarization behaviors of the nanocomposite dielectrics in many applications, particularly for capacitive energy storage. Predicting the relative permittivity of particle/polymer nanocomposites from the microstructu...

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Bibliographic Details
Main Authors: Shao-Long Zhong, Di-Fan Liu, Lei Huang, Yong-Xin Zhang, Qi Dong, Zhi-Min Dang
Format: Article
Language:English
Published: Tsinghua University Press 2022-12-01
Series:iEnergy
Subjects:
relative permittivity
nanocomposite dielectrics
convolutional neural networks
finite element method
prediction accuracy
Online Access:https://www.sciopen.com/article/10.23919/IEN.2022.0049

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https://www.sciopen.com/article/10.23919/IEN.2022.0049

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