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 high-throughput NDE techniques.

基于测得的位移/应变场识别弹性模量分布的反弹性问题在地质勘探，质量控制和医学诊断（例如弹性成像）中使用的各种无损评估（NDE）技术中起着关键作用。该领域的常规方法通常在计算上昂贵，并且不能满足对用于高级制造和临床实践的实时和高通量解决方案的日益增长的需求。在这里，我们提出了一种深度学习（DL）方法来应对这一挑战。通过构建代表性的剪切模量分布采样空间并采用条件生成对抗网络，我们证明DL模型可以通过在有限的采样空间上进行训练来学习应变和模量之间的高维映射。