Material Stiffness Prediction Based on Neural Network and Symbolic Regression

Jixuan Yi, Yiwen Li, Kai Zhang*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The effective stiffness of multi-inclusion composite materials is difficult to derive using theoretical methods since the localization relationships are usually complicated. In the past decades, researchers used numerical methods represented by the finite element method (FEM) to calculate the effective stiffness of multi-inclusion composites. However, FEM is time costly and cannot achieve real-time stiffness prediction. The emergence of machine learning methods provides a solution for realizing accurate real-time prediction, but making the machine learning model explainable in physical problems remains a crucial challenge. In this work, a machine learning method based on the convolutional neural network (CNN) and symbolic regression algorithm is used to achieve real-time stiffness prediction and give explicable expressions. The CNN model can predict the effective stiffness coefficients of multi-inclusion composites with all the mean absolute errors less than 2%. Using the data generated by the CNN model, the symbolic regression model based on the genetic algorithm can derive the expression of stiffness coefficients and provides a fast and explainable solution for stiffness prediction of multi-inclusion composites.

Original languageEnglish
Title of host publication2023 4th International Conference on Computer Engineering and Application, ICCEA 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages475-480
Number of pages6
ISBN (Electronic)9798350347548
DOIs
Publication statusPublished - 2023
Event4th International Conference on Computer Engineering and Application, ICCEA 2023 - Hangzhou, China
Duration: 7 Apr 20239 Apr 2023

Publication series

Name2023 4th International Conference on Computer Engineering and Application, ICCEA 2023

Conference

Conference4th International Conference on Computer Engineering and Application, ICCEA 2023
Country/TerritoryChina
CityHangzhou
Period7/04/239/04/23

Keywords

  • Machine learning
  • Multi-inclusion composites
  • Stiffness prediction
  • Symbolic regression

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