Publications

Selected publications

Prof Darryl Holm

[1] Darryl D Holm and Boris A Kupershmidt. Poisson brackets and Clebsch representations for magnetohydrodynamics, multifluid plasmas, and elasticity. Physica D: Nonlinear Phenomena 6.3 (1983), pp. 347–363.

[2] Darryl D Holm, Jerrold E Marsden, Tudor Ratiu, and Alan Weinstein. Nonlinear stability of fluid and plasma equilibria. Physics Reports 123.1-2 (1985), pp. 1–116.

[3] Darryl D Holm, Jerrold E Marsden, and Tudor S Ratiu. The Euler–Poincaré Equations and Semidirect Products with Applications to Continuum Theories. Advances in Mathematics 137.1 (1998), pp. 1–81.

[4] Darryl D Holm, Euler-Poincare dynamics of perfect complex fluids. In: Geometry, mechanics, and dynamics, edited by P. Newton, P. Holmes and A. Weinstein. Springer, pp. 113-167 (2002).

[5] Roberto Camassa and Darryl D Holm. An integrable shallow water equation with peaked solitons. Physical Review Letters 71.11 (1993), p. 1661.

[6] Antonio Degasperis, Darryl D Holm, and Andrew NW Hone. A new integrable equation with peakon solutions. Theoretical and Mathematical Physics 133.2 (2002), pp. 1463–1474. 

[7] Darryl D Holm and Jerrold E Marsden. Momentum maps and measure-valued solutions (peakons, filaments, and sheets) for the EPDiff equation. The breadth of symplectic and Poisson geometry. Springer, 2005, pp. 203– 235.

[8] Darryl D Holm, J Tilak Ratnanather, Alain Trouvé, and Laurent Younes. Soliton dynamics in computational anatomy. NeuroImage 23 (2004), S170–S178.

[9] Martins Bruveris, François Gay-Balmaz, Darryl D. Holm, and Tudor S. Ratiu. The momentum map representation of images. Journal of Nonlinear Science 21.1 (2011), pp. 115–150.

[10] Shiyi Chen, Ciprian Foias, Darryl D Holm, Eric Olson, Edriss S Titi, and Shannon Wynne. Camassa-Holm equations as a closure model for turbulent channel and pipe flow. Physical Review Letters 81.24 (1998), p. 5338.

[11] Ciprian Foias, Darryl D Holm, and Edriss S Titi. The three dimensional viscous Camassa–Holm equations, and their relation to the Navier–Stokes equations and turbulence theory. Journal of Dynamics and Differential Equations 14.1 (2002), pp. 1–35.

[12] Bernard J Geurts and Darryl D Holm. Leray and LANS-α modelling of turbulent mixing. Journal of Turbulence 7 (2006), N10. (WP2) [13] Darryl D Holm and Vakhtang Putkaradze. Aggregation of finite-size particles with variable mobility. Physical Review Letters 95.22 (2005), p. 226106.

[14] Ildar Gabitov, Darryl D Holm, Arnold Mattheus, and Benjamin P Luce. Recovery of solitons with nonlinear amplifying loop mirrors. Optics Letters 20.24 (1995), pp. 2490–2492.

[15] Daniel David, Darryl D Holm, and MV Tratnik. Hamiltonian chaos in nonlinear optical polarization dynamics. Physics Reports 187.6 (1990), pp. 281–367.

[16] Gennady P Berman, Gary D Doolen, Darryl D Holm, and Vladimir I Tsifrinovich. Quantum computer on a class of one-dimensional Ising systems. Physics Letters A 193.5-6 (1994), pp. 444–450.

[17] Darryl D Holm and Peter Lynch. Stepwise precession of the resonant swinging spring. SIAM Journal on Applied Dynamical Systems 1.1 (2002), 44–64 (electronic).

[18] RH Cushman, HR Dullin, A Giacobbe, DD Holm, M Joyeux, P Lynch, DA Sadovskii, BI Zhilinskiı. CO2 molecule as a quantum realization of the 1:1:2 resonant swing-spring with monodromy. Physical Review Letters 93.2 (2004), p. 024302.

[19] Darryl D Holm. Variational principles for stochastic fluid dynamics. Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences 471.2176 (2015).

[20] Dan Crisan, Franco Flandoli, and Darryl D. Holm. Solution properties of a 3D stochastic Euler fluid equation. Online at J Nonlinear Science, Preprint at arXiv:1704.06989 (2017).

[21] C. J. Cotter, G. A. Gottwald, and D. D. Holm, Stochastic partial differential fluid equations as a diffusive limit of deterministic Lagrangian multi-time dynamics, Proc Roy Soc A, 473: 20170388. Preprint at arXiv:1706.00287.

[22] Darryl D. Holm, YoungStatS: Transport Noise in Fluid Dynamics, IMS Bulletin, Vol. 53, issue 2 (2024); Available at: Institute of Mathematical Statistics | YoungStatS: Transport Noise in Fluid Dynamics (imstat.org)

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Prof Darryl Holm:

Prof Bertrand Chapron

[1] V. Resseguier, E. Mémin, D. Heitz and B. Chapron, Stochastic modeling and diffusion modes for POD models and small-scale flow analysis, Journ. of Fluid Mech., 828, 888-917, 2017.

[2] V. Resseguier, E. Mémin, B. Chapron, Geophysical flows under location uncertainty, Part I: Random transport and general models, Geophysical & Astrophysical Fluid Dynamics, 111(3): 149-176, 2017.

[3] V. Resseguier, E. Mémin, B. Chapron, Geophysical flows under location uncertainty, Part II: Quasigeostrophic models and efficient ensemble spreading, Geophysical & Astrophysical Fluid Dynamics, acceptepublication, 111(3): 177-208, 2017

[4] V. Resseguier, E. Mémin, B. Chapron, Geophysical flows under location uncertainty, Part III: SQG and frontal dynamics under strong turbulence, Geophysical & Astrophysical Fluid Dynamics, accepted for publication, 111(3): 209-227, 2017

[5] B. Chapron, P. Derain, E. Mémin, V. Resseguier, Large scale flows under location uncertainty: a consistent stocahstic framework,  Q. J. Roy. Met. Soc., 144(710), 251-260, 2018 

[6] F. Ardhuin et al., Measuring currents, ice drift, and waves from Space: the Sea surface Kinematics Multiscale monitoring (SKIM) concept, Ocean Science, 14(3), 337-354, 2018

[7] Rascle, N., J. Molemaker, L. Marié, F. Nouguier, B. Chapron, B. Lund, and A. Mouche,, Intense deformation field at oceanic front inferred from directional sea surface roughness observations, Geophys. Res. Lett., 44, 5599–5608, 2017

[8] Ardhuin, F., S. T. Gille, D. Menemenlis, C. B. Rocha, N. Rascle, B. Chapron, J. Gula, and J. Molemaker, Small-scale open ocean currents have large effects on wind wave heights, J. Geophys. Res. Oceans, 122, 4500– 4517, 2017

[9] Kudryavtsev, V., M. Yurovskaya, B. Chapron, F. Collard, and C. Donlon, Sun glitter imagery of surface waves. Part 2: Waves transformation on ocean currents, J. Geophys. Res. Oceans, 122, 1384–1399, 2017

[10] Rascle N., F. Nouguier, B. Chapron, A. Mouche and A. Ponte, Surface roughness changes by fine scale current gradients: Properties at multiple azimuth view angles, Journal of Physical Oceanography , 46(12), 368136942014, 2016.

[11] Rascle N., Chapron B., Ponte A., Ardhuin F. and P. Klein, Surface Roughness Imaging of Currents Shows Divergence and Strain in the Wind Direction, Journal of Physical Oceanography, 44(8), 2153-2163, 2014.

[12] Kudryavtsev, V., B. Chapron, and V. Makin, Impact of wind waves on the air-sea fluxes: A coupled model, J. Geophys. Res. Oceans, 119, 1217–1236, 2014.

[13] Ponte A, Klein P., Capet Xavier, Le Traon P.-Y., Chapron B., Lherminier P., Diagnosing Surface Mixed Layer Dynamics from High-Resolution Satellite Observations: Numerical Insights, Journal of Physical Oceanography, 43(7), 1345-1355, 2013

[14] Kudryavtsev, V., A. Myasoedov, B. Chapron, J. A. Johannessen, and F. Collard, Imaging mesoscale upper ocean dynamics using synthetic aperture radar and optical data, J. Geophys. Res., 117, C04029, 2012

[15] Collard, F., F. Ardhuin, and B. Chapron, Monitoring and analysis of ocean swell fields from space: New methods for routine observations, J. Geophys. Res., 114, C07023, 2009

[16] Ardhuin, F., B. Chapron, and F. Collard, Observation of swell dissipation across oceans, Geophys. Res. Lett., 36, L06607, 2009.

[17] Isern-Fontanet, J., B. Chapron, G. Lapeyre, and P. Klein, Potential use of microwave sea surface temperatures for the estimation of ocean currents, Geophys. Res. Lett., 33, L24608, 2006

[18] Chapron, B., F. Collard, and F. Ardhuin, Direct measurements of ocean surface velocity from space: Interpretation and validation, J. Geophys. Res., 110, C07008, 2005

Research Gate link

Prof Bertrand Chapron

Prof Dan Crisan

  1. O Lang, D. Crisan, Dan; É. Mémin, Analytical properties for a stochastic rotating shallow water model under location uncertainty, J. Math. Fluid Mech. 25, no. 2, 2023.
  2. D Crisan, M Ghil, Asymptotic behavior of the forecast–assimilation process with unstable dynamics, Chaos: An Interdisciplinary Journal of Nonlinear Science 33 (2), 1. 2023.
  3. D Crisan, O Lang, Well-posedness Properties for a Stochastic Rotating Shallow Water Model, Journal of Dynamics and Differential Equations, 1-31, 6, 2023
  4. D Crisan, DD Holm, E Luesink, PR Mensah, W Pan, Theoretical and computational analysis of the thermal quasi-geostrophic model, Journal of Nonlinear Science 33 (5), 96, 8, 2023.
  5. D. Crisan, D.D. Holm, O. Lang, P.R. Mensah, W. Pan, Theoretical analysis and numerical approximation for the stochastic thermal quasi-geostrophic model, Stochastics and Dynamics, 2023.
  6. D Crisan, DD Holm, P Korn, An implementation of Hasselmann’s paradigm for stochastic climate modelling based on stochastic Lie transport, Nonlinearity 36 (9), 4862, 2023.
  7. Lang, D Crisan, Well-posedness for a stochastic 2D Euler equation with transport noise, Stochastics and Partial Differential Equations: Analysis and Computations 11, 13, 2023.
  8. O Lang, PJ Van Leeuwen, D Crisan, R Potthast, Bayesian inference for fluid dynamics: a case study for the stochastic rotating shallow water model, Frontiers in Applied Mathematics and Statistics 8, 949354, 3, 2022.
  9. D Crisan, OD Street, On the analytical aspects of inertial particle motion, Journal of Mathematical Analysis and Applications 516 (1), 2022.
  10. D Crisan, DD Holm, JM Leahy, T Nilssen, Variational principles for fluid dynamics on rough paths, Advances in Mathematics 404, 108409, 10, 2022
  11. D Crisan, DD Holm, JM Leahy, T Nilssen, Solution properties of the incompressible Euler system with rough path advection, Journal of Functional Analysis 283 (9), 10963, 2, 2022.
  12. B Dufée, E Mémin, D Crisan, Stochastic parametrization: an alternative to inflation in Ensemble Kalman filters, Quarterly Journal of the Royal Meteorological Society 148 (744), 1075-1091, 3, 2022.
  13. O Lang, D Crisan, Well-posedness for a stochastic 2D Euler equation with transport noise, Stochastics and Partial Differential Equations: Analysis and Computations, 1-45, 2. 2022.
  14. D Crisan, O Lang, Local well-posedness for the great lake equation with transport noise, Rev Roumaine Math. Pures Appl. 66, 1, 131–155, 2021.
  15. D Crisan, DD Holm, OD Street, Wave–current interaction on a free surface, Studies in Applied Mathematics 147 (4), 1277-1338, 3, 2021.
  16. OD Street, D Crisan, Semi-martingale driven variational principles, Proceedings of the Royal Society A 477 (2247), 20200957 23, 2021.
  17. C Cotter, D Crisan, DD Holm, W Pan, I Shevchenko, A particle filter for stochastic advection by Lie transport: a case study for the damped and forced incompressible two-dimensional Euler equation, SIAM/ASA Journal on Uncertainty Quantification 8 (4), 1446-1492, 31, 2020.
  18. C Cotter, D Crisan, D Holm, W Pan, I Shevchenko, Data assimilation for a quasi-geostrophic model with circulation-preserving stochastic transport noise, Journal of Statistical Physics, 1-36, 2020
  19. D Crisan, F Flandoli, DD Holm, Solution properties of a 3D stochastic Euler fluid equation, Journal of Nonlinear Science 29 (3), 813-870, 60, 2019.
  20. C Cotter, D Crisan, DD Holm, W Pan, I Shevchenko, Numerically modeling stochastic Lie transport in fluid dynamics, Multiscale Modeling & Simulation 17 (1), 192-232, 27, 2019
  21. D Crisan, DD Holm,  Wave breaking for the Stochastic Camassa–Holm equation, Physica D: Nonlinear Phenomena 376, 138-143, 2018.

Google Scholar, ORCID and arXiv pages:

Prof Dan Crisan:

Prof Etienne Mémin

[1] Cai, S., Mémin, E., Dérian, P., Chao, X., (2018), Motion estimation under location uncertainty for turbulent flows, accepted for publication, Exp. In Fluids, 59(8). Use of stochastic transport equation to devise a parameter free accurate fluid motion estimator.

[2] Chapron, B., Dérian, P., Mémin, E., Resseguier, V., (2018), Large-scale flows under location uncertainty: a consistent stochastic framework, Quart. J. of Roy. Meteo. Soc., 144:    251    –    260. Derivation of a stochastic Lorenz-63 system though Holm-Mémin paradigm; demonstration of the relevance of the Holm-Memin theory in comparison with ad hoc forcing schemes. 

[3] S. Kadri-Harouna and E. Mémin (2017), Stochastic representation of the Reynolds transport theorem: revisiting large-scale modeling, Comp. and Fluids, 156 :456-469. Large-scale flow models derived from the Holm-Mémin theory.

[4] V. Resseguier, E. Mémin, D. Heitz and B. Chapron, Stochastic modeling and diffusion modes for POD models and small-scale flow analysis, Journ. of Fluid Mech., 828, 888-917, 2017. Setup of reduced order models and analysis of residual data.

[5] Y. Yang and E. Mémin, High-resolution data assimilation through stochastic subgrid tensor and parameter estimation from 4DEnVar, 2017, Tellus A, 69 (1), 2017. This paper describes how the Holm-Memin theory can be used to couple large scale models and high resolution data.

[6] V. Resseguier, E. Mémin, B. Chapron, Geophysical flows under location uncertainty, Part I: Random transport and general models, Geophysical & Astrophysical Fluid Dynamics, 111(3): 149-176, 2017. This paper develops the derivation of stochastic geophysical models of a stochastic PDE in the Holm-Memin theory.

[7] V. Resseguier, E. Mémin, B. Chapron, Geophysical flows under location uncertainty, Part II: Quasigeostrophic models and efficient ensemble spreading, Geophysical & Astrophysical Fluid Dynamics,111(3): 177-208, 2017

[8] V. Resseguier, E. Mémin, B. Chapron, Geophysical flows under location uncertainty, Part III: SQG and frontal dynamics under strong turbulence, Geophysical & Astrophysical Fluid Dynamics, accepted for publication, 111(3): 209-227, 2017 [9] Y. Yang, C. Robinson, D. Heitz and E., Mémin. Enhanced ensemble-based 4DVar scheme for data assimilation. Computer and Fluids, 115, 201--210, 2015. Definition of an efficient ensemble data assimilation strategy.

[10] A. Cuzol and E., Mémin. Monte carlo fixed-lag smoothing in state-space models. Nonlin. Processes Geophys., 21, 633-643, 2014.

[11] E. Mémin. Fluid flow dynamics under location uncertainty. Geophysical & Astrophysical Fluid Dynamics, 108(2): 119-146, 2014. Foundational paper of the Holm-Memin theory on flow dynamics represention from stochastic transport.

[12] C. Avenel, E. Mémin, P. Pérez. Stochastic level set dynamics to track closed curves through image data. Journ of Math. Imaging and Vision, 49:296-316., 2014.

[13] S. Kadri Harouna, P. Dérian, P. Héas, E. Mémin. Divergence-free Wavelets and High Order Regularization. International Journal of Computer Vision, 103(1):80-99, May 2013.

[14] S. Beyou, A. Cuzol, S. Gorthi, E. Mémin. Weighted Ensemble Transform Kalman Filter for Image Assimilation. Tellus A, 65(18803), January 2013.

[15] G. Artana, A. Cammilleri, J. Carlier, E. Mémin. Strong and weak constraint variational assimilation for reduced order fluid flow modeling. Journ. of Comp. Physics, 213(8):3264-3288, April 2012.

[16] N. Papadakis, E. Mémin, A. Cuzol, N. Gengembre. Data assimilation with the Weighted Ensemble Kalman Filter. Tellus-A, 62(5):673-697, 2010.

[17] Cuzol, E. Mémin. A stochastic filtering technique for fluid flows velocity fields tracking. IEEE Trans. on Pattern Anal.  and Mach. Intel., 31(7):1278-1293, 2009.

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Prof Etienne Mémin

STUOD Team

2020

  1. Stochastic modelling in fluid dynamics: Itô versus Stratonovich, Darryl D. Holm, Published: May 2020; https://doi.org/10.1098/rspa.2019.0812
  2. Stochastic representation of mesoscale eddy effects in coarse-resolution barotropic models, Werner Bauer, Pranav Chandramouli, Long Li, EtienneMémin, Published: July 2020: https://doi.org/10.1016/j.ocemod.2020.101646 
  3. Stochastic Variational Formulations of Fluid Wave-Current Interaction, Dan Crisan, Darryl D. Holm, James-Michael Leahy, Torstein Nilssen, Pubilished: April 2020: [2004.07829] Variational principles for fluid dynamics on rough paths (arxiv.org)
  4. Stochastic Variational Formulations of Fluid Wave-Current Interaction, Darryl D Holm, J Nonlinear Sci 31, 4 (2021), December 2020: https://doi.org/10.1007/s00332-020-09665-2  
  5. Stochastic wave-current interaction in thermal shallow water dynamics, Darryl D Holm, Erwin Luesink, Published: December 2020, [1910.10627] Stochastic wave-current interaction in thermal shallow water dynamics (arxiv.org)
  6. 4D large scale variational data assimilation of a turbulent flow with a dynamics error model, Pranav Chandramouli, Etienne Mémin, Dominique Heitz (2020), Journal of Computational Physics, 412, 109446, https://hal.inria.fr/hal-02547763/; DOI : 10.1016/j.jcp.2020.109446
  7. Deciphering the role of small-scale inhomogeneity on geophysical flow structuration: a stochastic approach, Werner Bauer, Pranav Chandramouli, Bertrand Chapron, Long Li, Etienne Mémin (2020)  Journal of Physical Oceanography, 50 (4), 983-1003, https://archimer.ifremer.fr/doc/00610/72194/70975.pdf 

2021

  1. Local well-posedness for the great lake equation with transport noise, Dan Crisan and Oana Lang, Published: January 2021, 10.pdf (imar.ro) 
  2. Log-Normalization Constant Estimation using the Ensemble Kalman-Bucy Filter with Application to High-Dimensional Models, Dan Crisan, Pierre Del Moral , Ajay Jasra & Hamza Ruzayqat, Published: January 2021, https://arxiv.org/pdf/2101.11460.pdf
  3. Stochastic linear modes in a turbulent channel flow Gilles Tissot, André Cavalieri, Etienne Mémin (2021), Journal of Fluid Mechanics, Volume 912. https://doi.org/10.1017/jfm.2020.1168; https://hal.inria.fr/hal-03081978/ 
  4. New trends in ensemble forecast strategy: uncertainty quantification for coarse-grid computational fluid dynamics, Valentin Resseguier, Long Li, Gabriel Jouan, Pierre Dérian, Etienne Mémin, Chapron Bertrand (2021), Archives of Computational Methods in Engineering, 28: 215–261, https://hal.inria.fr/hal-02558016/ DOI : 10.1007/s11831-020-09437-x
  5. Rotating shallow water flow under location uncertainty with a structure-preserving discretization,Rüdiger Brecht, Long Li, Werner Bauer, Etienne Mémin (2021), arXiv:2102.03783 [physics.flu-dyn], https://arxiv.org/abs/2102.03783/
  6. Stochastic Wave–Current Interaction in Thermal Shallow Water Dynamics, Darryl D. Holm and Erwin Luesink, Journal of Nonlinear Science volume 31, Article number: 29 (2021), https://doi.org/10.1007/s00332-021-09682-9
  7. Stochastic mesoscale circulation dynamics in the thermal ocean, Darryl D. Holm, Erwin Luesink, and Wei Pan, Phys. Fluids 33, 046603 (2021); doi.org/10.1063/5.0040026  
  8. Quantifying truncation-related uncertainties in unsteady fluid dynamics reduced order models, Valentin Resseguier, Agustin Picard, Etienne Mémin, Bertrand Chapron, SIAM/ASA Journal on Uncertainty Quantification (2021); https://hal.archives-ouvertes.fr/hal-03169957v2
  9. Testing a one-closure equation turbulence model in neutral boundary layers, Benoît Pinier, Roger Lewandowski, Etienne Mémin, Pranav Chandramouli, Computer Methods in Applied Mechanics and Engineering (2021), DOI: 10.1016/j.cma.2020.113662; https://hal.archives-ouvertes.fr/hal-01875464v4 
  10. Stochastic effects of waves on currents in the ocean mixed layer, Darryl D. Holm and Ruiao Hu, Journal of Mathematical Physics 62, 073102 (2021); https://doi.org/10.1063/5.0045010
  11. Wave–current interaction on a free surface, Dan Crisan, Darryl D. Holm, Oliver D. Street, Studies in Applied Mathematics published by Wiley Periodicals, (2021), https://onlinelibrary.wiley.com/doi/full/10.1111/sapm.12425
  12. Nonlinear dispersion in wave-current interactions, Darryl Holm, Ruiao Hu, August (2021) https://arxiv.org/pdf/2108.05213.pdf

2022

  1. Well-posedness for a stochastic 2D Euler equation with transport noise, Oana Lang and Dan Crisan, Stoch PDE: Anal Comp (2022), https://doi.org/10.1007/s40072-021-00233-7
  2. An application of the splitting-up method for the computation of a neural network representation for the solution for the filtering equations, Dan Crisan, Alexander Lobbe and Salvador Ortiz-Latorre,  Stoch PDE: Anal Comp (2022), https://doi.org/10.1007/s40072-022-00260-y 
  3. Analytical Properties for a Stochastic Rotating Shallow Water Model under Location Uncertainty, Oana Lang, Dan Crisan, Etienne Memin, June (2022), https://doi.org/10.48550/arXiv.2206.12451
  4. Bayesian inference for fluid dynamics: A case study for the stochastic rotating shallow water model, Oana Lang, Peter Jan van Leeuwen, Dan Crisan, Roland Potthast, Front. Appl. Math. Stat., 18 October 2022, Sec. Dynamical Systems, https://doi.org/10.3389/fams.2022.949354

2023

  1. Deterministic and stochastic Euler–Boussinesq convection, Darryl D. Holm and Wei Pan, Physica D 444 (2023) 133584, https://doi.org/10.1016/j.physd.2022.133584
  2. Conference proceedings: Stochastic Transport in Upper Ocean Dynamics (2023). https://link.springer.com/book/10.1007/978-3-031-18988-3 . This book is open access, which means that you have free and unlimited access. Brings selected, peer-reviewed studies in variability and uncertainty in upper ocean dynamics. Discusses means of quantifying the effects of local patterns of sea-level rise, heat uptake, carbon storage, and more. 
  3. Well-Posedness Properties for a Stochastic Rotating Shallow Water Model, Dan Crisan and Oana Lang, Journal of Dynamics and Differential Equations (2023), https://doi.org/10.1007/s10884-022-10243-1 
  4. Analytical Properties for a Stochastic Rotating Shallow Water Model Under Location Uncertainty, Oana Lang, Dan Crisan, Étienne Mémin, Journal of Mathematical Fluid Mechanics (2023), https://doi.org/10.1007/s00021-023-00769-9
  5. A Consistent Stochastic Large-Scale Representation of the Navier–Stokes Equations, Arnaud Debussche, Berenger Hug & Etienne Mémin, Journal of Mathematical Fluid Mechanics volume 25, Article number: 19 (2023), https://doi.org/10.1007/s00021-023-00764-0
  6. Stochastic data-driven parameterization of unresolved eddy effects in a baroclinic quasi-geostrophic model. Li, L., Deremble, B., Lahaye, N., & Mémin, E. (2023). Journal of Advances in Modeling Earth Systems, 15, e2022MS003297. https://doi.org/10.1029/2022MS003297
  7. An implementation of Hasselmann's paradigm for stochastic climate modelling based on stochastic Lie transport, D. Crisan, D. Holm and P. Korn, (2023), Nonlinearity 36 4862, DOI 10.1088/1361-6544/ace1ce 
  8. Existence and uniqueness of maximal solutions to SPDEs with applications to viscous fluid equations, Daniel Goodair, Dan Crisan, Oana Lang, Stochastics and Partial Differential Equations: Analysis and Computations (2023), 10.1007/s40072-023-00305-w.
  9. Theoretical analysis and numerical approximation for the stochastic thermal quasi-geostrophic model, Dan Crisan, Darryl Holm, Oana Lang, Romeo Mensah, Wei Pan, Stochastic & Dynamics, 10.1142/S02194937235003.
  10. Geometric Mechanics of the Vertical Slice, Darryl D. Holm, Ruiao Hu, and Oliver D. Street, For Volume 1, Issue 1 of Geometric Mechanics (2023) https://arxiv.org/pdf/2309.02602.pdf
  11. Dan Crisan, Oana Lang, Alexander Lobbe, Peter-Jan van Leeuwen, Roland Potthast. Noise calibration for SPDEs: A case study for the rotating shallow water model. Foundations of Data Science (2023). doi: 10.3934/fods.2023012