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pro vyhledávání: '"Kapllani, Lorenc"'
Autor:
Kapllani, Lorenc, Teng, Long
In this work, we present a novel forward differential deep learning-based algorithm for solving high-dimensional nonlinear backward stochastic differential equations (BSDEs). Motivated by the fact that differential deep learning can efficiently appro
Externí odkaz:
http://arxiv.org/abs/2408.05620
Autor:
Kapllani, Lorenc, Teng, Long
In this work, we propose a novel backward differential deep learning-based algorithm for solving high-dimensional nonlinear backward stochastic differential equations (BSDEs), where the deep neural network (DNN) models are trained not only on the inp
Externí odkaz:
http://arxiv.org/abs/2404.08456
Deep learning-based numerical schemes for solving high-dimensional backward stochastic differential equations (BSDEs) have recently raised plenty of scientific interest. While they enable numerical methods to approximate very high-dimensional BSDEs,
Externí odkaz:
http://arxiv.org/abs/2310.03393
Autor:
Kapllani, Lorenc, Teng, Long
Publikováno v:
Discrete Contin. Dyn. Syst. - B, 29 (2024) 1695-1729
In this work, we propose a new deep learning-based scheme for solving high dimensional nonlinear backward stochastic differential equations (BSDEs). The idea is to reformulate the problem as a global optimization, where the local loss functions are i
Externí odkaz:
http://arxiv.org/abs/2010.01319
Autor:
Kapllani, Lorenc, Teng, Long
Publikováno v:
J. Math. Industry 12 (2022)
The goal of this work is to parallelize the multistep scheme for the numerical approximation of the backward stochastic differential equations (BSDEs) in order to achieve both, a high accuracy and a reduction of the computation time as well. In the m
Externí odkaz:
http://arxiv.org/abs/1909.13560
Autor:
Kapllani, Lorenc, Teng, Long
Publikováno v:
Discrete & Continuous Dynamical Systems - Series B; Apr2024, Vol. 29 Issue 4, p1-35, 35p
Akademický článek
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Autor:
Kapllani, Lorenc1 (AUTHOR) kapllani@math.uni-wuppertal.de, Teng, Long1 (AUTHOR)
Publikováno v:
Journal of Mathematics in Industry. 1/28/2022, Vol. 12 Issue 1, p1-22. 22p.