The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.
Deep learning model to predict complex stress and strain fields in hierarchical composites
Open accessAbstract
Deep learning predicts mechanical fields in hierarchical composites, as an alternative to conventional numerical methods.
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Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Image-to-Image Translation with Conditional Adversarial Networks
Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.632 · Cited by 22,930
A review on polymer nanofibers by electrospinning and their applications in nanocomposites
Huang, Z.-M., Zhang, Y.-Z., Kotaki, M., & Ramakrishna, S. (2003) · Composites Science and Technology · doi:10.1016/s0266-3538(03)00178-7 · Cited by 7,556
Bradski, G. R. (2000) · Medical Entomology and Zoology · Cited by 5,994
Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces
Behler, J., & Parrinello, M. (2007) · Physical Review Letters · doi:10.1103/physrevlett.98.146401 · Cited by 5,010
Machine learning for molecular and materials science
Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O., & Walsh, A. (2018) · Nature · doi:10.1038/s41586-018-0337-2 · Cited by 4,968
Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
Gómez-Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., Aguilera-Iparraguirre, J., Hirzel, T. D., Adams, R. P., & Aspuru-Guzik, A. (2018) · ACS Central Science · doi:10.1021/acscentsci.7b00572 · Cited by 3,106
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Fiber-reinforced composites : materials, manufacturing, and design
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Cited by
Battery Safety: Machine Learning-Based Prognostics
Zhao, J., Feng, X., Pang, Q., Fowler, M., Lian, Y., Ouyang, M., & Burke, A. F. (2024) · Progress in Energy and Combustion Science · doi:10.1016/j.pecs.2023.101142 · Cited by 216
Ni, B., & Buehler, M. J. (2024) · Extreme Mechanics Letters · doi:10.1016/j.eml.2024.102131 · Cited by 113
Self-citationMechanical properties prediction of composite laminate with FEA and machine learning coupled method
Zhang, C., Li, Y., Jiang, B., Wang, R., Liu, Y., & Jia, L. (2022) · Composite Structures · doi:10.1016/j.compstruct.2022.116086 · Cited by 102
Metadata
- Authors
- Yang, Z., Yu, C.-H., & Buehler, M. J.
- Venue
- Science Advances
- Published
- 2021-04-09
- Volume
- 7
- Issue
- 15
- ISSN
- 2375-2548 (ISSN-L)
- OpenAlex
- W3145017754
- Cited by
- 389
- References
- 45
- FWCI
- 16.71
- Type
- journal article
- Language
- en
- Open access
- gold
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- Yang, Z., Yu, C.-H., & Buehler, M. J. (2021). Deep learning model to predict complex stress and strain fields in hierarchical composites. Science Advances. https://doi.org/10.1126/sciadv.abd7416
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
