Update data-parallel PINNs citation to Scientific Reports

docs/user/parallel.rst

@@ -62,6 +62,10 @@ To obtain more information concerning data-parallel PINNs, we refer the readers
   @article{escapil2023h,
     title={h-analysis and data-parallel physics-informed neural networks},
     author={Escapil-Inchausp{\'e}, Paul and Ruz, Gonzalo A},
-    journal={arXiv preprint arXiv:2302.08835},
-    year={2023}
+    journal={Scientific Reports},
+    volume={13},
+    number={1},
+    pages={17562},
+    year={2023},
+    doi={10.1038/s41598-023-44541-5}
   }

docs/user/research.rst

@@ -522,7 +522,7 @@ PINN
 #. J\. M. Tucny, M. Durve, A. Montessori, and S. Succi. `Learning of viscosity functions in rarefied gas flows with physics-informed neural networks <https://www.sciencedirect.com/science/article/pii/S0045793023003390>`_. *Computers Fluids*, 269:106114, 2024.
 #. P\. Brendel, V. Medvedev, & A. Rosskopf. `Physics-informed neural networks for magnetostatic problems on axisymmetric transformer geometries <https://ieeexplore.ieee.org/abstract/document/10373037>`_. *IEEE Journal of Emerging and Selected Topics in Industrial Electronics*, 2023.
 #. T\. Zhang, D. Wang, & Y. Lu. `RheologyNet: A physics-informed neural network solution to evaluate the thixotropic properties of cementitious materials <https://doi.org/10.1016/j.cemconres.2023.107157>`_. *Cement and Concrete Research*, 168, 107157, 2023.
-#. S\. C. Salas, A. O. Alvarado, F. Ortega-culaciati, & P. escapil-inchauspé. `Physics informed neural network for quasistatic fault slip forward and inverse problems <https://ci2ma.udec.cl/wonapde2024/abstract/Cobaise.pdf>`_. 2023.
+#. S\. C. Salas, A. O. Alvarado, F. Ortega-Culaciati, & P. Escapil-Inchauspé. `Physics informed neural network for quasistatic fault slip forward and inverse problems <https://ci2ma.udec.cl/wonapde2024/abstract/Cobaise.pdf>`_. 2023.
 #. C\. Li, & Z. Han. `Shallow water equations-fused dam-break wave propagation prediction model ensembled with a training process resampling method <https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10396666>`_. *2023 International Conference on Intelligent Computing and Next Generation Networks（ICNGN)*, 1-6. 10.1109/ICNGN59831.2023.10396666. 
 #. X\. Yang, Y. Du, L. Li, Z. Zhou, & X. Zhang. `Physics-informed neural network for model prediction and dynamics parameter identification of collaborative robot joints <https://ieeexplore.ieee.org/document/10305255>`_. *IEEE Robotics and Automation Letters*, vol. 8, no. 12, pp. 8462-8469, 2023.
 #. S\. H. Radbakhsh, K. Zandi, & M. Nik-bakht. `Physics-informed neural network for analyzing elastic beam behavior <https://www.dpi-proceedings.com/index.php/shm2023/article/view/36810>`_. *Structural Health Monitoring*, 2023.
@@ -646,6 +646,7 @@ PINN
 #. A\. Ncube. `Investigating new computational approaches for solving black hole perturbation equations <https://ujcontent.uj.ac.za/view/pdfCoverPage?instCode=27UOJ_INST&filePid=136787410007691&download=true>`_. *Doctoral dissertation, University of Johannesburg*, 2022.
 #. C\. Garcıa-Cervera, M. Kessler, & F. Periago. `A first step towards controllability of partial differential equations via physics-informed neural networks <https://www.researchgate.net/profile/Francisco-Periago/publication/359524458_A_first_step_towards_controllability_of_partial_differential_equations_via_physics-informed_neural_networks/links/6242e24c8068956f3c56d679/A-first-step-towards-controllability-of-partial-differential-equations-via-physics-informed-neural-networks.pdf>`_. 2022.
 #. L\. Guo, H. Wu, X. Yu, & T. Zhou. `Monte Carlo PINNs: Deep learning approach for forward and inverse problems involving high dimensional fractional partial differential equations <https://arxiv.org/pdf/2203.08501.pdf>`_. *arXiv preprint arXiv:2203.08501*, 2022.
+#. P\. Escapil-Inchauspé, & G. A. Ruz. `h-Analysis and data-parallel physics-informed neural networks <https://doi.org/10.1038/s41598-023-44541-5>`_. *Scientific Reports*, 13, 17562, 2023.
 #. P\. Escapil-Inchauspé, & G. A. Ruz. `Hyper-parameter tuning of physics-informed neural networks: Application to Helmholtz problems <https://doi.org/10.1016/j.neucom.2023.126826>`_. *Neurocomputing*, 126826, 2023.
 #. P\. Escapil-Inchauspé, & G. Ruz. `Physics-informed neural networks for operator equations with stochastic data <https://arxiv.org/abs/2211.10344>`_. *arXiv preprint arXiv:2211.10344*, 2022.
 #. H\. Xie, C. Zhai, L. Liu, & H. Yong. `A weighted first-order formulation for solving anisotropic diffusion equations with deep neural networks <https://arxiv.org/pdf/2205.06658.pdf>`_. *arXiv preprint arXiv:2205.06658*, 2022.