March 10, 2022, 2:12 a.m. | Jiequn Han, Jihao Long

cs.LG updates on arXiv.org arxiv.org

The recently proposed numerical algorithm, deep BSDE method, has shown
remarkable performance in solving high-dimensional forward-backward stochastic
differential equations (FBSDEs) and parabolic partial differential equations
(PDEs). This article lays a theoretical foundation for the deep BSDE method in
the general case of coupled FBSDEs. In particular, a posteriori error
estimation of the solution is provided and it is proved that the error
converges to zero given the universal approximation capability of neural
networks. Numerical results are presented to demonstrate the …

arxiv convergence math pr

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Principal Applied Scientist

@ Microsoft | Redmond, Washington, United States

Data Analyst / Action Officer

@ OASYS, INC. | OASYS, INC., Pratt Avenue Northwest, Huntsville, AL, United States