A deep learning approach to estimate stress distribution: a fast and accurate surrogate of finite-element analysis

Liang Liang, Minliang Liu, Caitlin Martin, Wei Sun

Research output: Contribution to journalArticlepeer-review

118 Scopus citations

Abstract

Structural finite-element analysis (FEA) has been widely used to study the biomechanics of human tissues and organs, as well as tissue–medical device interactions, and treatment strategies. However, patient-specific FEA models usually require complex procedures to set up and long computing times to obtain final simulation results, preventing prompt feedback to clinicians in time-sensitive clinical applications. In this study, by using machine learning techniques, we developed a deep learning (DL) model to directly estimate the stress distributions of the aorta. The DL model was designed and trained to take the input of FEA and directly output the aortic wall stress distributions, bypassing the FEA calculation process. The trained DL model is capable of predicting the stress distributions with average errors of 0.492% and 0.891% in the Von Mises stress distribution and peak Von Mises stress, respectively. This study marks, to our knowledge, the first study that demonstrates the feasibility and great potential of using the DL technique as a fast and accurate surrogate of FEA for stress analysis.

Original languageEnglish (US)
Article number20170844
JournalJournal of the Royal Society Interface
Volume15
Issue number138
DOIs
StatePublished - Jan 1 2018
Externally publishedYes

Keywords

  • Deep learning
  • Finite-element analysis
  • Neural network
  • Stress analysis

ASJC Scopus subject areas

  • Biotechnology
  • Biophysics
  • Bioengineering
  • Biomaterials
  • Biochemistry
  • Biomedical Engineering

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