Physics-Informed Neural Networks for Nonlinear Analysis of Reinforced Concrete Beams
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-60271-9_24
Reference10 articles.
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2. Shen, C., et al.: Differentiable modelling to unify machine learning and physical models for geosciences. Nat. Rev. Earth Environ. (2023). https://doi.org/10.1038/s43017-023-00450-9
3. Raissi, M., Perdikaris, P., Karniadakis, G.E.: Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys. 378, 686–707 (2019). https://doi.org/10.1016/j.jcp.2018.10.045
4. Bastek, J.-H., Kochmann, D.M.: Physics-informed neural networks for shell structures. Eur. J. Mech. – A Solids 97, 104849 (2023). https://doi.org/10.1016/j.euromech-sol.2022.104849
5. Zhou, M., Mei, G.: Transfer learning-based coupling of smoothed finite element method and physics-informed neural network for solving elastoplastic inverse problems. Mathematics 11(11), 2529 (2023). https://doi.org/10.3390/math11112529
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