Update docs

Author: lululxvi

README.md

@@ -27,8 +27,8 @@ DeepXDE is a library for scientific machine learning and physics-informed learni
     - DeepONet extensions, e.g., POD-DeepONet [[Comput. Methods Appl. Mech. Eng.](https://doi.org/10.1016/j.cma.2022.114778)]
     - physics-informed DeepONet [[Sci. Adv.](https://doi.org/10.1126/sciadv.abi8605)]
     - MIONet: learning multiple-input operators [[arXiv](https://arxiv.org/abs/2202.06137)]
+    - 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)]
-    - multifidelity DeepONet [[arXiv](https://arxiv.org/abs/2204.06684)]
 - multifidelity neural network (MFNN)
     - learning from multifidelity data [[J. Comput. Phys.](https://doi.org/10.1016/j.jcp.2019.109020), [PNAS](https://doi.org/10.1073/pnas.1922210117)]
 

docs/index.rst

@@ -19,8 +19,8 @@ DeepXDE
     - DeepONet extensions, e.g., POD-DeepONet [`Comput. Methods Appl. Mech. Eng. <https://doi.org/10.1016/j.cma.2022.114778>`_]
     - physics-informed DeepONet [`Sci. Adv. <https://doi.org/10.1126/sciadv.abi8605>`_]
     - MIONet: learning multiple-input operators [`arXiv <https://arxiv.org/abs/2202.06137>`_]
+    - 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>`_]
-    - multifidelity DeepONet [`arXiv <https://arxiv.org/abs/2204.06684>`_]
 - multifidelity neural network (MFNN)
     - learning from multifidelity data [`J. Comput. Phys. <https://doi.org/10.1016/j.jcp.2019.109020>`_, `PNAS <https://doi.org/10.1073/pnas.1922210117>`_]
 

docs/user/research.rst

@@ -46,7 +46,7 @@ PINN
 DeepONet
 --------
 
-#. L. Lu, R. Pestourie, S. Johnson, & G. Romano. `Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of nanoscale heat transport <https://arxiv.org/abs/2204.06684>`_. *arXiv preprint arXiv:2204.06684*, 2022.
+#. L. Lu, R. Pestourie, S. Johnson, & G. Romano. `Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of nanoscale heat transport <https://doi.org/10.1103/PhysRevResearch.4.023210>`_. *Physical Review Research*, 4(2), 023210, 2022.
 #. L. Lu, X. Meng, S. Cai, Z. Mao, S. Goswami, Z. Zhang, & G. Karniadakis. `A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data <https://doi.org/10.1016/j.cma.2022.114778>`_. *Computer Methods in Applied Mechanics and Engineering*, 393, 114778, 2022.
 #. P. Jin, S. Meng, & L. Lu. `MIONet: Learning multiple-input operators via tensor product <https://arxiv.org/abs/2202.06137>`_. *arXiv preprint arXiv:2202.06137*, 2022.
 #. C. Lin, M. Maxey, Z. Li, & G. Karniadakis. `A seamless multiscale operator neural network for inferring bubble dynamics <https://doi.org/10.1017/jfm.2021.866>`_. *Journal of Fluid Mechanics*, 929, A18, 2021.