Physics Informed Cellular Neural Networks for Solving Partial Differential Equations
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-53212-2_3
Reference8 articles.
1. F.Chen, D. Sondak, P. Protopapas, et al. Neurodiffeq: A python package for solving differential equations with neural networks. J. Open Source Softw. 5:46, 1931, 2020.
2. L.O.Chua, L. Yang. Cellular Neural Network: Theory and Applications. IEEE Trans. CAS. vol. 35, p.1257, 1988.
3. L. Lu, X. Meng, Z. Mao, et al. DeepXDE: A deep learning library for solving differential equations. SIAM Rev. 63:1, 208–228, 2021.
4. S. Mishra, R. Molinaro. Estimates on the generalization error of physics-informed neural networks for approximating a class of inverse problems for PDEs. IMA J. Numer. Anal. ,2021.
5. S.Mishra, R. Molinaro. Estimates on the generalization error of physics-informed neural networks for approximating PDEs. IMA J. Numer. Anal., p drab093, 2022.
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