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 Zhilinski─▒. 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.

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

Prof Dan Crisan

[1] J.M.C. Clark, Crisan D., “On a robust version of the integral representation formula of nonlinear filtering”, Probability Theory and Related Fields, Vol 133, No 1 pp 43-56, 2005.

[2] Crisan, D., “Particle approximations for a class of stochastic partial differential equations”, Applied Mathematics and Optimization Journal, Vol 54, No 3, pp 293-317, 2006.

[3]  Crisan, D., Heine, K., “Stability of the discrete time filter in terms of the tails of noise distributions”, Journal of the London Mathematical Society, Vol. 78, No 2, pp 441-458, 2008. 

[4]  Crisan, D., Kouritzin, M. A., Xiong, J., Nonlinear filtering with signal dependent observation noise, Electronic Journal of Probability pp 1863-1883, 2009.

[5]  Crisan, D., Xiong, J., Approximate McKean-Vlasov representations for a class of SPDEs, Stochastics 82 , no. 1-3, 53–68, 2010.

[6] Crisan, D.; Obanubi, O. Particle filters with random resampling times. Stochastic Process. Appl.  122  (2012),  no. 4, 1332–1368.

[7] Crisan, Dan; Ortiz-Latorre, Salvador A Kusuoka-Lyons-Victoir particle filter. Proc. R. Soc. Lond. Ser. A Math. Phys. Eng. Sci.  469  (2013),  no. 2156, 20130076, 19 pp.

[8] Crisan, D.; Diehl, J.; Friz, P. K.; Oberhauser, H. Robust filtering: correlated noise and multidimensional observation. Ann. Appl. Probab.  23  (2013),  no. 5, 2139–2160.

[9] Beskos, Alexandros; Crisan, Dan O.; Jasra, Ajay; Whiteley, Nick Error bounds and normalising constants for sequential Monte Carlo samplers in high dimensions. Adv. in Appl. Probab.  46, 2014.

[10]  Beskos, Alexandros; Crisan, Dan; Jasra, Ajay On the stability of sequential Monte Carlo methods in high dimensions. Ann. Appl. Probab.  24  (2014),  no. 4, 1396–1445.

[11] Crisan, Dan; Xiong, Jie Numerical solution for a class of SPDEs over bounded domains. Stochastics  86  (2014),  no. 3, 450–472.

[12]  Crisan, Dan; Míguez, Joaquín Particle-kernel estimation of the filter density in state-space models. Bernoulli  20  (2014),  no. 4, 1879–1929. This paper contains methodology that allows for data assimilation and parameter estimation to be performed at the same time.

[13]  Crisan, Dan; Otobe, Yoshiki; Peszat, Szymon Inverse problems for stochastic transport equations. Inverse Problems  31  (2015),  no. 1, 20 pp.

[14]  Beskos, Alexandros; Crisan, Dan; Jasra, Ajay; Kamatani, Kengo; Zhou, Yan; A stable particle filter for a class of high-dimensional state-space models. Adv. in Appl. Probab. 49, 2017.

[15] D Crisan, J Miguez,  Uniform convergence over time of a nested particle filtering scheme for recursive parameter estimation in state--space Markov models, Adv. in Appl. Probab.  49  (2017),  no. 4, 1170–1200. A sequel to [12].

[16] D Paulin, A Jasra, D Crisan, A Beskos, On Concentration Properties of Partially Observed Chaotic Systems, Adv. in Appl. Probab.  50 (2018),  no. 2, 440–479.

[17] D Crisan, F Flandoli, DD Holm, Solution properties of a 3D stochastic Euler fluid equation, in print, J. Nonlin Science This paper contains the first theoretical analysis of a stochastic PDE obtained through the Holm-Memin theory.

[18] D. Crisan, DD Holm, Wave breaking for the Stochastic Camassa-Holm equation, Physica D  376/377  (2018), 138–143.

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.


  1. Stochastic modelling in fluid dynamics: Itô versus Stratonovich, Darryl D. Holm, Published: May 2020;
  2. Stochastic representation of mesoscale eddy effects in coarse-resolution barotropic models, Werner Bauer, Pranav Chandramouli, Long Li, EtienneMémin, Published: July 2020: 
  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 (
  4. Stochastic Variational Formulations of Fluid Wave-Current Interaction, Darryl D Holm, J Nonlinear Sci 31, 4 (2021), December 2020:  
  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 (
  6. Local well-posedness for the great lake equation with transport noise, Dan Crisan and Oana Lang, Published: January 2021, 10.pdf ( 
  7. 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,
  8. Stochastic linear modes in a turbulent channel flow Gilles Tissot, André Cavalieri, Etienne Mémin (2021), Journal of Fluid Mechanics, Volume 912.; 
  9. 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, DOI : 10.1007/s11831-020-09437-x
  10. 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],
  11. 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,; DOI : 10.1016/
  12. 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, 
  13. 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),
  14. Stochastic mesoscale circulation dynamics in the thermal ocean, Darryl D. Holm, Erwin Luesink, and Wei Pan, Phys. Fluids 33, 046603 (2021);  
  15. 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);
  16. 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; 
  17. Stochastic effects of waves on currents in the ocean mixed layer, Darryl D. Holm and Ruiao Hu, Journal of Mathematical Physics 62, 073102 (2021);
  18. Wave–current interaction on a free surface, Dan Crisan, Darryl D. Holm, Oliver D. Street, Studies in Applied Mathematics published by Wiley Periodicals, (2021),   
  19. Nonlinear dispersion in wave-current interactions, Darryl Holm, Ruiao Hu, August 2021