Performance evaluation of deep learning approaches for predicting mechanical fields in composites
Author:
Funder
Air Force Office of Scientific Research
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00366-024-01966-4.pdf
Reference20 articles.
1. Wang Y, Oyen D, Guo WG, Mehta A, Scott CB, Panda N, Fernández-Godino MG, Srinivasan G, Yue X (2021) StressNet—deep learning to predict stress with fracture propagation in brittle materials. npj Mater Degrad 5(1):1–10. https://doi.org/10.1038/s41529-021-00151-y
2. Jiang H, Nie Z, Yeo R, Farimani AB, Kara LB (2021) StressGAN: a generative deep learning model for two-dimensional stress distribution prediction. J Appl Mech 88(5):051005. https://doi.org/10.1115/1.4049805
3. Feng H, Prabhakar P (2021) Difference-based deep learning framework for stress predictions in heterogeneous media. Compos Struct 269:113957. https://doi.org/10.1016/j.compstruct.2021.113957
4. Bhaduri A, Gupta A, Graham-Brady L (2022) Stress field prediction in fiber-reinforced composite materials using a deep learning approach. Compos B Eng 238:109879. https://doi.org/10.1016/j.compositesb.2022.109879
5. Yang Z, Yu CH, Guo K, Buehler MJ (2021) End-to-end deep learning method to predict complete strain and stress tensors for complex hierarchical composite microstructures. J Mech Phys Solids 154:104506. https://doi.org/10.1016/j.jmps.2021.104506
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