Fetching the paper…
Reading the bibliography…
We propose a numerical method for solving high dimensional fully nonlinear partial differential equations (PDEs).
“Adapted solution of a backward stochastic differential equation”
E. Pardoux and S. Peng · 1990
Earlier work this paper cites.
“Approximation theory of the MLP model in neural networks”
A. Pinkus · 1999
Earlier work this paper cites.
“Stochastic volatility with an Ornstein-Uhlenbeck process and extension”
R. Sch“”obel and J. Zhu · 1999
Earlier work this paper cites.
“A solution approach to valuation with unhedgeable risks”
T. Zariphopoulou · 2001
Earlier work this paper cites.
“Second-order backward stochastic differential equations and fully nonlinear parabolic PDEs”
P. Cheridito, H.M. Soner, N. Touzi and N. Victoir · 2007
Earlier work this paper cites.
“Continuous-time Stochastic Control and Optimization with Financial Applications” 61
H. Pham · 2009
Earlier work this paper cites.
“A probabilistic numerical method for fully nonlinear parabolic PDEs”
A. Fahim, N. Touzi and X. Warin · 2011
Earlier work this paper cites.
“A splitting method for fully nonlinear degenerate parabolic PDEs”
X. Tan · 2013
Earlier work this paper cites.
D.. Kingma and J. Ba · 2014
Cited alongside, same era.
“TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems” Software available from tensorflow.org, 2015
M. Abadi et al · 2015
Cited alongside, same era.
“Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations”
W. E, J. Han and A. Jentzen · 2017
Cited alongside, same era.
“Solving Nonlinear and High-Dimensional Partial Differential Equations via Deep Learning”
A. Al-Aradi et al · 2018
Cited alongside, same era.
“Solving high-dimensional partial differential equations using deep learning”
J. Han, A. Jentzen and W. E · 2018
“Monte Carlo for high-dimensional degenerated Semi Linear and Full Non Linear PDEs”
X. Warin · 2018
Later among the works it cites.
“Deep splitting method for parabolic PDEs”
C. Beck et al · 2019
Closest in time.
“Machine Learning Approximation Algorithms for High-Dimensional Fully Nonlinear Partial Differential Equations and Second-order Backward Stochastic Differential Equations”
C. Beck, W. E and A. Jentzen · 2019
Closest in time.
“Machine learning for semi linear PDEs”
Quentin Chan-Wai-Nam, Joseph Mikael and Xavier Warin · 2019
Closest in time.
“Overcoming the curse of dimensionality for some Hamilton–Jacobi partial differential equations via neural network architectures”
J. Darbon, G. Langlois and T. Meng · 2020
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
M. Hutzenthaler et al · 2018
Cited alongside, same era.
“DGM: A deep learning algorithm for solving partial differential equations”
J. Sirignano and K. Spiliopoulos · 2018
Cited alongside, same era.
“Unbiased deep solvers for parametric PDEs”
M. Vidales, D. Siska and L. Szpruch · 2018
Cited alongside, same era.
“Deep backward schemes for high-dimensional nonlinear PDEs”
C“ˆome Hur“’e, Huy“ˆen Pham and Xavier Warin · 2020
Closest in time.
N. N“”usken and L. Richter · 2020
Closest in time.