Accurate prediction of discontinuous crack paths in random porous media via a generative deep learning model Article Swipe
Yuxiang He
,
Yu Tan
,
Mingshan Yang
,
Yongbin Wang
,
Yangguang Xu
,
Jianghong Yuan
,
Xiangyu Li
,
Weiqiu Chen
,
Guozheng Kang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1073/pnas.2413462121
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1073/pnas.2413462121
Pore structures provide extra freedoms for the design of porous media, leading to desirable properties, such as high catalytic rate, energy storage efficiency, and specific strength. This unfortunately makes the porous media susceptible to failure. Deep understanding of the failure mechanism in microstructures is a key to customizing high-performance crack-resistant porous media. However, solving the fracture problem of the porous materials is computationally intractable due to the highly complicated configurations of microstructures. To bridge the structural configurations and fracture responses of random porous media, a unique generative deep learning model is developed. A two-step strategy is proposed to deconstruct the fracture process, which sequentially corresponds to elastic deformation and crack propagation. The geometry of microstructure is translated into a scalar of elastic field as an intermediate variable, and then, the crack path is predicted. The neural network precisely characterizes the strong interactions among pore structures, the multiscale behaviors of fracture, and the discontinuous essence of crack propagation. Crack paths in random porous media are accurately predicted by simply inputting the images of targets, without inputting any additional input physical information. The prediction model enjoys an outstanding performance with a prediction accuracy of 90.25% and possesses a robust generalization capability. The accuracy of the present model is a record so far, and the prediction is accomplished within a second. This study opens an avenue to high-throughput evaluation of the fracture behaviors of heterogeneous materials with complex geometries.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1073/pnas.2413462121
- OA Status
- green
- Cited By
- 17
- References
- 46
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4402832330Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1073/pnas.2413462121Digital Object Identifier
- Title
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Accurate prediction of discontinuous crack paths in random porous media via a generative deep learning modelWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-09-25Full publication date if available
- Authors
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Yuxiang He, Yu Tan, Mingshan Yang, Yongbin Wang, Yangguang Xu, Jianghong Yuan, Xiangyu Li, Weiqiu Chen, Guozheng KangList of authors in order
- Landing page
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https://doi.org/10.1073/pnas.2413462121Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://www.ncbi.nlm.nih.gov/pmc/articles/11459186Direct OA link when available
- Concepts
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Porous medium, Materials science, Fracture (geology), Artificial neural network, Deformation (meteorology), Computer science, Porosity, Generative grammar, Mechanics, Artificial intelligence, Composite material, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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17Total citation count in OpenAlex
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2025: 17Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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