Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/148674
Title: A deep learning approach to the inverse problem of modulus identification in elasticity
Authors: Ni, Bo
Gao, Huajian
Keywords: Engineering::Mechanical engineering
Issue Date: 2021
Source: Ni, B. & Gao, H. (2021). A deep learning approach to the inverse problem of modulus identification in elasticity. MRS Bulletin, 46, 19-25. https://dx.doi.org/10.1557/s43577-020-00006-y
Journal: MRS Bulletin
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.
URI: https://hdl.handle.net/10356/148674
ISSN: 0883-7694
DOI: 10.1557/s43577-020-00006-y
Rights: © 2021 Materials Research Society. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:MAE Journal Articles

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