Prof Darryl Holm
 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.
 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.
 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.
 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).
 Roberto Camassa and Darryl D Holm. An integrable shallow water equation with peaked solitons. Physical Review Letters 71.11 (1993), p. 1661.
 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.
 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.
 Darryl D Holm, J Tilak Ratnanather, Alain Trouvé, and Laurent Younes. Soliton dynamics in computational anatomy. NeuroImage 23 (2004), S170–S178.
 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.
 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.
 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.
 Bernard J Geurts and Darryl D Holm. Leray and LANS-α modelling of turbulent mixing. Journal of Turbulence 7 (2006), N10. (WP2)  Darryl D Holm and Vakhtang Putkaradze. Aggregation of finite-size particles with variable mobility. Physical Review Letters 95.22 (2005), p. 226106.
 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.
 Daniel David, Darryl D Holm, and MV Tratnik. Hamiltonian chaos in nonlinear optical polarization dynamics. Physics Reports 187.6 (1990), pp. 281–367.
 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.
 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).
 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.
 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).
 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).
 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
 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.
 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.
 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
 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
 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
 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
 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
 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
 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
 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.
 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.
 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.
 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
 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
 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
 Ardhuin, F., B. Chapron, and F. Collard, Observation of swell dissipation across oceans, Geophys. Res. Lett., 36, L06607, 2009.
 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
 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
 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.
 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.
 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.
 Crisan, D., Kouritzin, M. A., Xiong, J., Nonlinear filtering with signal dependent observation noise, Electronic Journal of Probability pp 1863-1883, 2009.
 Crisan, D., Xiong, J., Approximate McKean-Vlasov representations for a class of SPDEs, Stochastics 82 , no. 1-3, 53–68, 2010.
 Crisan, D.; Obanubi, O. Particle filters with random resampling times. Stochastic Process. Appl. 122 (2012), no. 4, 1332–1368.
 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.
 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.
 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.
 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.
 Crisan, Dan; Xiong, Jie Numerical solution for a class of SPDEs over bounded domains. Stochastics 86 (2014), no. 3, 450–472.
 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.
 Crisan, Dan; Otobe, Yoshiki; Peszat, Szymon Inverse problems for stochastic transport equations. Inverse Problems 31 (2015), no. 1, 20 pp.
 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.
 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 .
 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.
 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.
 D. Crisan, DD Holm, Wave breaking for the Stochastic Camassa-Holm equation, Physica D 376/377 (2018), 138–143.
Prof Etienne Mémin
 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.
 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.
 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.
 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.
 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.
 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.
 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
 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  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.
 A. Cuzol and E., Mémin. Monte carlo fixed-lag smoothing in state-space models. Nonlin. Processes Geophys., 21, 633-643, 2014.
 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.
 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.
 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.
 S. Beyou, A. Cuzol, S. Gorthi, E. Mémin. Weighted Ensemble Transform Kalman Filter for Image Assimilation. Tellus A, 65(18803), January 2013.
 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.
 N. Papadakis, E. Mémin, A. Cuzol, N. Gengembre. Data assimilation with the Weighted Ensemble Kalman Filter. Tellus-A, 62(5):673-697, 2010.
 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.