Abstract
The inverse elasticity problem of identifying elastic modulus distribution based on measured displacement/strain fields plays a key role in various non-destructive evaluation (NDE) techniques used in geological exploration, quality control, and medical diagnosis (e.g., elastography). Conventional methods in this field are often computationally costly and cannot meet the increasing demand for real-time and high-throughput solutions for advanced manufacturing and clinical practices. Here, we propose a deep learning (DL) approach to address this challenge. By constructing representative sampling spaces of shear modulus distribution and adopting a conditional generative adversarial net, we demonstrate that the DL model can learn high-dimensional mapping between strain and modulus via training over a limited portion of the sampling space. The proposed DL approach bypasses the costly iterative solver in conventional methods and can be rapidly deployed with high accuracy, making it particularly suitable for applications such as real-time elastography and highthroughput NDE techniques.
Similar content being viewed by others
Change history
18 February 2021
This article was updated to include the abstract
Author information
Authors and Affiliations
Corresponding author
Supplementary Information
Below is the link to the electronic supplementary material.
Supplementary Video 1 (MP4 26 MB)
Supplementary Video 2 (MP4 14 MB)
Rights and permissions
About this article
Cite this article
Ni, B., Gao, H. A deep learning approach to the inverse problem of modulus identification in elasticity. MRS Bulletin 46, 19–25 (2021). https://doi.org/10.1557/s43577-020-00006-y
Published:
Issue Date:
DOI: https://doi.org/10.1557/s43577-020-00006-y