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As is known, an option price is a solution to a certain partial differential equation (PDE) with terminal conditions (payoff functions).
Approximation by superposition of a sigmoidal function
G. Cybenko · 1989
Earlier work this paper cites.
Adapted solution of a backward stochastic differential equation
Etienne Pardous, Shige Peng · 1990
Earlier work this paper cites.
Options, Futures and other Derivatives, 9th Edition
John C. Hull · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe, Christian Szegedy · 2015
Earlier work this paper cites.
Monte Carlo Method and Stochastic Processes: from Linear to non-Linear
Emmanuel Gobet · 2016
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Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations
Weinan E, Jiequn Han, Arnulf Jantzen · 2017
Cited alongside, same era.
Machine learning approximation algorithms for high dimensional fully nonlinear partial differential equations and second order backward stochastic differential equations
Chritian Beck, Weinan E, Arnulf Jantzen · 2017
Cited alongside, same era.
Asymptotic expansion as prior knowledge in deep learning method for high dimensional BSDEs
Masaaki Fujii, Akihiko Takahashi, Masayuki Takahashi · 2017
Cited alongside, same era.
Forward backward stochastic neural networks: Deep learning of high-dimensional partial differential equations
Maziar Raissi · 2018
Later among the works it cites.
Machine learning for semi linear PDEs
Quentin Chan-Wai-Nam, Joseph Mikael, Xavier Warin · 2018
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Deep learning-based BSDE solver for LIBOR market model with application to Bermudan swaption pricing and hedging
Haojie Wang, Han Chen, Agus Sudjianto, Richard Liu, Qi Shen · 2018
Later among the works it cites.
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