From 634ef2d1d67e42a4b13b4af298bd475810cf5f98 Mon Sep 17 00:00:00 2001
From: "Navid C. Constantinou" <navidcy@users.noreply.github.com>
Date: Fri, 12 Jul 2024 17:49:37 +0200
Subject: [PATCH] Update papers using GeophysicalFlows.jl list (#363)

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 docs/src/index.md | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/docs/src/index.md b/docs/src/index.md
index bc38beef..fb086f33 100644
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@@ -84,11 +84,13 @@ The bibtex entry for the paper is:
 
 ## Papers using `GeophysicalFlows.jl`
 
+1. Pudig, M. and Smith, K. S. (2024) Baroclinic turbulence above rough topography: The vortex gas and topographic turbulence regimes. _ESS Open Archive_, doi:[10.22541/essoar.171995116.60993353/v1](https://doi.org/10.22541/essoar.171995116.60993353/v1).
+
 1. Shokar, I. J. S., Haynes, P. H. and Kerswell, R. R. (2024) Extending deep learning emulation across parameter regimes to assess stochastically driven spontaneous transition events. In ICLR 2024 Workshop on AI4DifferentialEquations in Science. url: [https://openreview.net/forum?id=7a5gUX4e5q](https://openreview.net/forum?id=7a5gUX4e5q).
 
 1. He, J. and Wang, Y. (2024) Multiple states of two-dimensional turbulence above topography. arXiv preprint arXiv:2405.10826, doi:[10.48550/arXiv.2405.10826](https://doi.org/10.48550/arXiv.2405.10826).
 
-1. Parfenyev, V., Blumenau, M., and Nikitin, I. (2024) Inferring parameters and reconstruction of two-dimensional turbulent flows with physics-informed neural networks. arXiv preprint arXiv:2404.01193, doi:[10.48550/arXiv.2404.01193](https://doi.org/10.48550/arXiv.2404.01193).
+1. Parfenyev, V., Blumenau, M., and Nikitin, I. (2024) Enhancing capabilities of particle image/tracking velocimetry with physics-informed neural networks. arXiv preprint arXiv:2404.01193, doi:[10.48550/arXiv.2404.01193](https://doi.org/10.48550/arXiv.2404.01193).
 
 1. Shokar, I. J. S., Kerswell, R. R., and Haynes, P. H. (2024) Stochastic latent transformer: Efficient modeling of stochastically forced zonal jets. _Journal of Advances in Modeling Earth Systems_, **16**, e2023MS004177, doi:[10.1029/2023MS004177](https://doi.org/10.1029/2023MS004177).