2022
DOI: 10.23919/ien.2022.0049
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Prediction on the relative permittivity of energy storage composite dielectrics using convolutional neural networks: A fast and accurate alternative to finite-element method

Abstract: 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 microstructure is of great significance. However, the classical effective medium theory and physicsbased numerical calculation represented by finite element method are time-consuming and cumbersome for complex structures and no… Show more

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Cited by 2 publications
(1 citation statement)
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“…The new experiences gained from them can also help guide the targeted optimisation of dielectric materials [33]. Specifically, these lessons derived from the data sonification results can inform the AI-based design [34][35][36] of dielectric materials during model construction. In addition to the learned data itself, the models incorporate more specialised knowledge and perform better as a result [37].…”
Section: Discussionmentioning
confidence: 99%
“…The new experiences gained from them can also help guide the targeted optimisation of dielectric materials [33]. Specifically, these lessons derived from the data sonification results can inform the AI-based design [34][35][36] of dielectric materials during model construction. In addition to the learned data itself, the models incorporate more specialised knowledge and perform better as a result [37].…”
Section: Discussionmentioning
confidence: 99%