A branching particle system approximation for a class of FBSDEs

DOI10.1186/s41546-016-0007-yzbMath1440.60062OpenAlexW2556342349WikidataQ59438036 ScholiaQ59438036MaRDI QIDQ2296088

Dejian Chang, Jie Xiong, Hui Li Liu

Publication date: 17 February 2020

Published in: Probability, Uncertainty and Quantitative Risk (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1186/s41546-016-0007-y

zbMATH Keywords

numerical solutionpartial differential equationsforward-backward stochastic differential equationbranching particle system

Mathematics Subject Classification ID

Stochastic partial differential equations (aspects of stochastic analysis) (60H15) Computational methods for stochastic equations (aspects of stochastic analysis) (60H35) Branching processes (Galton-Watson, birth-and-death, etc.) (60J80)

Related Items (6)

Overcoming the curse of dimensionality in the approximative pricing of financial derivatives with default risks ⋮ Overcoming the curse of dimensionality in the numerical approximation of backward stochastic differential equations ⋮ An overview on deep learning-based approximation methods for partial differential equations ⋮ On multilevel Picard numerical approximations for high-dimensional nonlinear parabolic partial differential equations and high-dimensional nonlinear backward stochastic differential equations ⋮ Machine learning approximation algorithms for high-dimensional fully nonlinear partial differential equations and second-order backward stochastic differential equations ⋮ Algorithms for solving high dimensional PDEs: from nonlinear Monte Carlo to machine learning

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