Update docs: Add Fourier-MIONet (#1246)

Author: Jerry-Jzy

README.md

@@ -27,6 +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-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

@@ -19,6 +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-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

@@ -50,6 +50,7 @@ DeepXDE has been used in
   `University of Rome Tor Vergata <http://web.uniroma2.it>`_,
   `Chalmers University of Technology <https://www.chalmers.se/en>`_,
   `Wuhan University of Technology <http://english.whut.edu.cn>`_,
+  `University of Delaware <https://www.udel.edu>`_,
   `University of Kentucky <https://www.uky.edu>`_,
   `University of Surrey <https://www.surrey.ac.uk>`_,
   `Federal University of Rio de Janeiro <https://ufrj.br/en>`_,
@@ -101,6 +102,7 @@ DeepXDE has been used in
   `Anailytica <https://anailytica.com>`_,
   `Ansys <https://www.ansys.com>`_,
   `BirenTech Research <https://www.birentech.com>`_,
+  `ExxonMobil <https://corporate.exxonmobil.com>`_,
   `General Motors <https://www.gm.com>`_,
   `RocketML <https://www.rocketml.net>`_,
   `Saudi Aramco <https://www.aramco.com>`_
@@ -200,6 +202,7 @@ PINN
 DeepONet
 --------
 
+#. 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.
 #. K\. Kobayashi, J. Daniell, & S. Alam. `Operator learning framework for digital twin and complex engineering systems <https://arxiv.org/pdf/2301.06701.pdf>`_. *arXiv e-prints*, pp.arXiv-2301, 2023.
 #. E\. Pickering, S. Guth, G. Karniadakis, & T. Sapsis. `Discovering and forecasting extreme events via active learning in neural operators <https://www.nature.com/articles/s43588-022-00376-0>`_. *Nature Computational Science*, 2(12), pp.823-833, 2022.