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

By L. Liang, Minliang Liu, C. Martin, Wei Sun. Journal of the Royal Society Interface, 2018

437 citations recorded by the source corpus.

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.

Subfields: Civil and Structural Engineering, Mechanical Engineering, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/80a0bf3ac3064ba6d8186f7447b42aaa7bf3bfcf

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Citations in the corpus

Deep Learning of Subsurface Flow via Theory-guided Neural Network (2019, methodology): “In addition to the field of AI, DNN has also been utilized in diverse scientific disciplines, including the fields of biomedicine (Liang et al., 2018; Shashikumar et al., 2018), economics (Singh & Srivastava, 2017; Yong et al., 2017), chemistry (Fooshee et al., 2018; Sun et al., 2019), and physics…”
A Physics-Informed Machine Learning Approach for Solving Heat Transfer Equation in Advanced Manufacturing and Engineering Applications (2020, neutral): “, 2020b, 2020a, 2019b), and stress analysis (Liang et al., 2018).”
Recent advances and applications of surrogate models for finite element method computations: a review (2022, methodology): “In a similar study, Liang et al. (2018) showed the ability of surrogates based on deep neural networks to predict the aortic wall stress distributions.”
Deep Generative Models in Engineering Design: A Review (2021, methodology): “Even performance evaluation using conventional simulation tools like FEA or CFD can require an intermediate conversion from an image to a 3D model.”
Physics-Informed Neural Network for Modelling the Thermochemical Curing Process of Composite-Tool Systems During Manufacture (2020, background): “This theory-guided machine learning approach [20–23] requires a significant amount of training data from expensive high-fidelity simulations.”