Ghaffari Motlagh, Y., Fathi, F., Brigham, J.C. et al. (1 more author) (2025) Deep learning for inverse material characterisation. Computer Methods in Applied Mechanics and Engineering, 436. 117650. ISSN 0045-7825
Abstract
This paper presents an approach for computationally efficient inverse material characterization using Physics-Informed Neural Networks (PINNs) based on partial-field response measurements. PINNs reconstruct the full spatial distribution of the system’s response from the measured portion of the response field and estimate the spatial distribution of unknown material properties. The primary computational expense in this approach is the one-time generation of potential responses for the PINNs, resulting in significant computational efficiency. Furthermore, this study utilizes PINNs to train a model based on the underlying physics described by differential equations, and to quantify aleatoric uncertainty arising from noisy data. We demonstrate several one-dimensional and two-dimensional examples where the elastic modulus distribution is characterized based on static partial-field displacement response measurements. The inversion procedure efficiently provides accurate estimates of material property distributions, showcasing the potential of PINNs in practical applications.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2025 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
Keywords: | Inverse problem; Direct inversion, PINNs, Material characterization |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Computation Science & Engineering |
Depositing User: | Symplectic Publications |
Date Deposited: | 09 Dec 2024 15:06 |
Last Modified: | 14 Jan 2025 16:03 |
Published Version: | https://www.sciencedirect.com/science/article/pii/... |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.cma.2024.117650 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:220567 |
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