A. Al-aradi, Solving Nonlinear and High-Dimensional Partial Differential Equations via Deep Learning, 2018.

M. Abadi, Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org. 2015

C. Beck, Deep splitting method for parabolic PDEs, 2019.

C. Beck, W. E. , and A. Jentzen, Machine learning approximation algorithms for high-dimensional fully nonlinear partial differential equations and second-order backward stochastic differential equations, 2017.

P. Cheridito, Second-order backward stochastic differential equations and fully nonlinear parabolic PDEs, Communications on Pure and Applied Mathematics, vol.60, pp.1081-1110, 2007.

A. Fahim, N. Touzi, and X. Warin, A probabilistic numerical method for fully nonlinear parabolic PDEs, The Annals of Applied Probability, pp.1322-1364, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00367103

C. Huré, H. Pham, and X. Warin, Some machine learning schemes for high-dimensional nonlinear PDEs, 2019.

A. Pinkus, Approximation theory of the MLP model in neural networks, Acta numerica, vol.8, pp.143-195, 1999.

J. Sirignano and K. Spiliopoulos, DGM: A deep learning algorithm for solving partial differential equations, Journal of Computational Physics, vol.375, pp.1339-1364, 2018.

X. Tan, A splitting method for fully nonlinear degenerate parabolic PDEs, In: Electronic Journal of Probability, vol.18, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01246999

X. Warin, Monte Carlo for high-dimensional degenerated Semi Linear and Full Non Linear PDEs, 2018.