Update docs for Fourier-DeepONet (#1434)

README.md

@@ -27,7 +27,7 @@ DeepXDE is a library for scientific machine learning and physics-informed learni
     - DeepONet: learning operators [[Nat. Mach. Intell.](https://doi.org/10.1038/s42256-021-00302-5)]
     - DeepONet extensions, e.g., POD-DeepONet [[Comput. Methods Appl. Mech. Eng.](https://doi.org/10.1016/j.cma.2022.114778)]
     - MIONet: learning multiple-input operators [[SIAM J. Sci. Comput.](https://doi.org/10.1137/22M1477751)]
-    - Fourier-DeepONet [[arXiv](https://arxiv.org/abs/2305.17289)], Fourier-MIONet [[arXiv](https://arxiv.org/abs/2303.04778)]
+    - Fourier-DeepONet [[Comput. Methods Appl. Mech. Eng.](https://doi.org/10.1016/j.cma.2023.116300)], Fourier-MIONet [[arXiv](https://arxiv.org/abs/2303.04778)]
     - physics-informed DeepONet [[Sci. Adv.](https://doi.org/10.1126/sciadv.abi8605)]
     - multifidelity DeepONet [[Phys. Rev. Research](https://doi.org/10.1103/PhysRevResearch.4.023210)]
     - DeepM&Mnet: solving multiphysics and multiscale problems [[J. Comput. Phys.](https://doi.org/10.1016/j.jcp.2021.110296), [J. Comput. Phys.](https://doi.org/10.1016/j.jcp.2021.110698)]

docs/index.rst

@@ -19,7 +19,7 @@ DeepXDE
     - DeepONet: learning operators [`Nat. Mach. Intell. <https://doi.org/10.1038/s42256-021-00302-5>`_]
     - DeepONet extensions, e.g., POD-DeepONet [`Comput. Methods Appl. Mech. Eng. <https://doi.org/10.1016/j.cma.2022.114778>`_]
     - MIONet: learning multiple-input operators [`SIAM J. Sci. Comput. <https://doi.org/10.1137/22M1477751>`_]
-    - Fourier-DeepONet [`arXiv <https://arxiv.org/abs/2305.17289>`_], Fourier-MIONet [`arXiv <https://arxiv.org/abs/2303.04778>`_]
+    - Fourier-DeepONet [`Comput. Methods Appl. Mech. Eng. <https://doi.org/10.1016/j.cma.2023.116300>`_], Fourier-MIONet [`arXiv <https://arxiv.org/abs/2303.04778>`_]
     - physics-informed DeepONet [`Sci. Adv. <https://doi.org/10.1126/sciadv.abi8605>`_]
     - multifidelity DeepONet [`Phys. Rev. Research <https://doi.org/10.1103/PhysRevResearch.4.023210>`_]
     - DeepM&Mnet: solving multiphysics and multiscale problems [`J. Comput. Phys. <https://doi.org/10.1016/j.jcp.2021.110296>`_, `J. Comput. Phys. <https://doi.org/10.1016/j.jcp.2021.110698>`_]

docs/user/research.rst

@@ -210,7 +210,7 @@ PINN
 DeepONet
 --------
 
-#. M\. Zhu, S. Feng, Y. Lin, & L. Lu. `Fourier-DeepONet: Fourier-enhanced deep operator networks for full waveform inversion with improved accuracy, generalizability, and robustness <https://arxiv.org/abs/2305.17289>`_. *arXiv preprint arXiv:2305.17289*, 2023.
+#. M\. Zhu, S. Feng, Y. Lin, & L. Lu. `Fourier-DeepONet: Fourier-enhanced deep operator networks for full waveform inversion with improved accuracy, generalizability, and robustness <https://doi.org/10.1016/j.cma.2023.116300>`_. *Computer Methods in Applied Mechanics and Engineering*, 416, 116300, 2023.
 #. S\. Mao, R. Dong, L. Lu, K. M. Yi, S. Wang, & P. Perdikaris. `PPDONet: Deep operator networks for fast prediction of steady-state solutions in disk-planet systems <https://iopscience.iop.org/article/10.3847/2041-8213/acd77f>`_. *The Astrophysical Journal Letters*, 950(2), L12, 2023.
 #. Z\. Jiang, M. Zhu, D. Li, Q. Li, Y. Yuan, & L. Lu. `Fourier-MIONet: Fourier-enhanced multiple-input neural operators for multiphase modeling of geological carbon sequestration <https://arxiv.org/pdf/2303.04778.pdf>`_. *arXiv preprint arXiv:2303.04778*, 2023.
 #. S\. Wang, & P. Perdikaris. `Long-time integration of parametric evolution equations with physics-informed deeponets <https://www.sciencedirect.com/science/article/pii/S0021999122009184>`_. *Journal of Computational Physics*, 475, p.111855, 2023.