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

HIGHLIGHTS

• who: Shao-Long Zhong and colleagues from the (UNIVERSITY) have published the research work: Prediction on the relative permittivity of energy storage composite dielectrics using convolutional neural networks: A fast and accurate alternative to finite-element method, in the Journal: (JOURNAL)
• what: Through numerical experiments the authors demonstrate that the trained network can efficiently provide an accurate agreement between the ConvNet model and FEM by virtue of the significant evaluation metrics R2 which reaches as high as 0.9783 and 0.9375 on training and testing data respectively. In this work, a method to predict . . .
  

   

  

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