T. Wu and M. Tegmark, Toward an AI physicist for unsupervised learning, 2018.

R. Iten, T. Metger, H. Wilming, L. Del-rio, and R. Renner, Discovering physical concepts with neural networks, 2018.

S. Greydanus, M. Dzamba, and J. Yosinski, Hamiltonian neural networks, 2019.

C. Wang, H. Zhai, and Y. You, Emergent schrödinger equation in an introspective machine learning architecture, Science Bulletin, 2019.

G. Carleo, I. Cirac, K. Cranmer, L. Daudet, M. Schuld et al., Machine learning and the physical sciences, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02101667

J. L. Steven-l-brunton, J. Proctor, and . Kutz, Discovering governing equations from data by sparse identification of nonlinear dynamical systems, Proceedings of the National Academy of Sciences, vol.113, issue.15, pp.3932-3937, 2016.

M. Raissi, Deep hidden physics models: Deep learning of nonlinear partial differential equations, The Journal of Machine Learning Research, vol.19, issue.1, pp.932-955, 2018.

Z. Long, Y. Lu, and B. Dong, Pde-net 2.0: Learning pdes from data with a numeric-symbolic hybrid deep network, Journal of Computational Physics, p.108925, 2019.

Y. Peter, S. Lu, M. Kim, and . Solja?i?, Extracting interpretable physical parameters from spatiotemporal systems using unsupervised learning, 2019.

A. Emmanuel-de-bezenac, P. Pajot, and . Gallinari, Deep learning for physical processes: Incorporating prior scientific knowledge, 2017.

I. Ayed, E. De-bézenac, A. Pajot, J. Brajard, and P. Gallinari, Learning dynamical systems from partial observations, 2019.

S. Ouala, D. Nguyen, L. Drumetz, B. Chapron, A. Pascual et al., Learning latent dynamics for partially-observed chaotic systems, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02274705

C. Ubelmann, P. Klein, and L. Fu, Dynamic interpolation of sea surface height and potential applications for future high-resolution altimetry mapping, Journal of Atmospheric and Oceanic Technology, vol.32, issue.1, pp.177-184, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01132400

U. Naumann, J. Utke, C. Wunsch, C. Hill, M. Heimbach et al., Adjoint code by source transformation with openad/f, Proceedings of the European Conference on Computational Fluid Dynamics (ECCOMAS CFD, 2006.

J. Utke, U. Naumann, M. Fagan, N. Tallent, M. Strout et al., Openad/f: A modular open-source tool for automatic differentiation of fortran codes, ACM Transactions on Mathematical Software (TOMS), vol.34, issue.4, p.18, 2008.

A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang et al., Automatic differentiation in pytorch, 2017.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis et al., Tensorflow: A system for large-scale machine learning, 12th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 16), pp.265-283, 2016.

. Atilim-gunes-baydin, A. Barak, A. Pearlmutter, J. M. Andreyevich-radul, and . Siskind, Automatic differentiation in machine learning: a survey, Journal of machine learning research, vol.18, issue.153, 2018.

E. Weinan, A proposal on machine learning via dynamical systems, Communications in Mathematics and Statistics, vol.5, issue.1, pp.1-11, 2017.

F. Rousseau, L. Drumetz, and R. Fablet, Residual networks as flows of diffeomorphisms, Journal of Mathematical Imaging and Vision, pp.1-11, 2019.
URL : https://hal.archives-ouvertes.fr/hal-01796729

G. K. Vallis, Atmospheric and Oceanic Fluid Dynamics, 2006.

S. Smith and G. K. Vallis, The scales and equilibration of midocean eddies: Freely evolving flow, Journal of Physical Oceanography, vol.31, issue.2, pp.554-571, 2001.

. Bl-hua, Numerical simulations of the vertical structure of quasi-geostrophic turbulence, Journal of the atmospheric sciences, vol.43, issue.23, pp.2923-2936, 1986.

L. Fu and . Glenn-r-flierl, Nonlinear energy and enstrophy transfers in a realistically stratified ocean, Dynamics of Atmospheres and Oceans, vol.4, issue.4, pp.219-246, 1980.

J. R. Shewchuk, An introduction to the conjugate gradient method without the agonizing pain, 1994.

J. Molines, meom-configurations/NATL60-CJM165: NATL60 code used for CJM165 experiment, 2018.