Add research papers (#502)

Author: anranjiao

docs/user/research.rst

@@ -7,13 +7,21 @@ PINN
 ----
 
 #. A. Sacchetti, B. Bachmann, K. Loffel, U.-M. Kunzi, & B. Paoli. `Neural networks to solve partial differential equations: a comparison with finite elements <https://arxiv.org/abs/2201.03269>`_. *arXiv preprint arXiv:2201.03269*, 2022.
+#. C. H. Martin, A. Oved, R. A. Chowdhury, E. Ullmann, N. S. Peters, A. A. Bharath, & M. Varela.  `EP-PINNs: Cardiac electrophysiology characterisation using physics-informed neural networks <https://arxiv.org/abs/2112.07703>`_. *arXiv preprint arXiv:2112.07703*, 2021.
+#. K. F. Iversen.  `Physics informed neural networks for inverse advection-diffusion problems <https://bora.uib.no/bora-xmlui/handle/11250/2835305>`_. *The University of Bergen*, 2021.
+#. S. Markidis. `The old and the new: Can physics-informed deep-learning replace traditional linear solvers? <https://www.frontiersin.org/articles/10.3389/fdata.2021.669097/full>`_. *Frontiers in Big Data*, 4:669097, 2021.
+#. S. Alkhadhr, X. Liu, & M. Almekkawy. `Modeling of the forward wave propagation using physics-informed neural networks <https://doi.org/10.1109/IUS52206.2021.9593574>`_. *2021 IEEE International Ultrasonics Symposium (IUS)*, pp. 1–4, 2021.
 #. L. Lu, R. Pestourie, W. Yao, Z. Wang, F. Verdugo, & S. G. Johnson. `Physics-informed neural networks with hard constraints for inverse design <https://doi.org/10.1137/21M1397908>`_. *SIAM Journal on Scientific Computing*, 43(6), B1105--B1132, 2021. [`Code <https://github.com/lululxvi/hpinn>`_]
+#. Z. Li, H. Zheng, N. Kovachki, D. Jin, H. Chen, B. Liu, K. Azizzadenesheli, & A. Anandkumar. `Physics-informed neural operator for learning partial differential equations <https://arxiv.org/abs/2111.03794>`_. *arXiv preprint arXiv:2111.03794*, 2021.
 #. J. Yu, L. Lu, X. Meng, & G. E. Karniadakis. `Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems <https://arxiv.org/abs/2111.02801>`_. *arXiv preprint arXiv:2111.02801*, 2021.
+#. C. P. Hennigan. `The primal Hamiltonian: A new global approach to monetary policy <https://www.colorado.edu/economics/sites/default/files/attached-files/21-02_-_hennigan.pdf>`_. 2021.
 #. S. Lee, & T. Kadeethum. `Physics-informed neural networks for solving coupled flow and transport system <http://ceur-ws.org/Vol-2964/article_197.pdf>`_. 2021.
 #. Y. Chen, & L. Dal Negro. `Physics-informed neural networks for imaging and parameter retrieval of photonic nanostructures from near-field data <https://arxiv.org/abs/2109.12754>`_. *arXiv preprint arXiv:2109.12754*, 2021.
 #. A. M. Ncube, G. E. Harmsen, & A. S. Cornell. `Investigating a new approach to quasinormal modes: Physics-informed neural networks <https://arxiv.org/abs/2108.05867>`_. *arXiv preprint arXiv:2108.05867*, 2021.
 #. M. Almajid, & M. Abu-Alsaud. `Prediction of porous media fluid flow using physics informed neural networks <https://doi.org/10.1016/j.petrol.2021.109205>`_. *Journal of Petroleum Science and Engineering*, 109205, 2021.
+#. E. J. Whalen. `Enhancing surrogate models of engineering structures with graph-based and physics-informed learning <https://dspace.mit.edu/handle/1721.1/139609>`_. *PhD dissertation, Massachusetts Institute of Technology*, 2021.
 #. M. Merkle. `Boosting the training of physics-informed neural networks with transfer learning <https://github.com/mariusmerkle/TL-PINNs/blob/main/Bachelor%20Thesis.pdf>`_. 2021. [`Code <https://github.com/mariusmerkle/TL-PINNs>`_]
+#. A. Warey, T. Han, & S. Kaushik.  `Investigation of numerical diffusion in aerodynamic flow simulations with physics informed neural networks <https://arxiv.org/abs/2103.03115>`_. *arXiv preprint arXiv:2103.03115*, 2021.
 #. L. Lu, X. Meng, Z. Mao, & G. E. Karniadakis. `DeepXDE: A deep learning library for solving differential equations <https://doi.org/10.1137/19M1274067>`_. *SIAM Review*, 63(1), 208--228, 2021. [`Code <https://github.com/lululxvi/deepxde/tree/master/examples>`_]
 #. A. Yazdani, L. Lu, M. Raissi, & G. E. Karniadakis. `Systems biology informed deep learning for inferring parameters and hidden dynamics <https://doi.org/10.1371/journal.pcbi.1007575>`_. *PLoS Computational Biology*, 16(11), e1007575, 2020. [`Code <https://github.com/alirezayazdani1/SBINNs>`_]
 #. Q. Zhang, Y. Chen, & Z. Yang. `Data driven solutions and discoveries in mechanics using physics informed neural network <https://www.preprints.org/manuscript/202006.0258>`_. *Preprints*, 2020060258, 2020.