Herr Lorenc Kapllani

Mathematics

Biografie

Brief biographical details:

t.b.a.

Title and abstract of thesis project:

Deep learning algorithms for solving high-dimensional nonlinear backward stochastic differential equations and their uncertainty quantification

Neuigkeiten

Papers already presented:

• Kapllani, L., Rottmann, M. and Teng, L., Uncertainty quantification in deep learning schemes for backward stochastic differential equations. In International Conference of Computational Finance, Wuppertal 2022.
• Kapllani, L. and Teng, L., Deep learning algorithms for solving high-dimensional nonlinear backward stochastic differential equations. In European Consortium for Mathematics in Industry, Wuppertal 2021.
• Kapllani, L. and Teng, L., Multistep schemes for solving backward stochastic differential equations on gpu. In International Conference of Computational Finance, A Coruna 2019.  

Publications:

• Kapllani, L. (2022). The effect of the number of neural networks on deep learning schemes for solving high dimensional nonlinear backward stochastic differential equations. In European Consortium for Mathematics in Industry, pages 67–73. Springer.
• Kapllani, L. and Teng, L. (2022). Multistep schemes for solving backward stochastic differential equations on gpu. J. Math. Industry, 12(1):1–22.
• Kapllani, L. and Teng, L. (2020). Deep learning algorithms for solving high dimensional nonlinear backward stochastic differential equations. arXiv preprint arXiv:2010.01319.
• Kapllani, L., Teng, L., and Ehrhardt, M. (2019). A multistep scheme to solve backward stochastic differential equations for option pricing on gpus. In Advances in High Performance Computing, pages 196–208. Springer.

Other academic activities and memberships:

• Participated in 18^th winter school on Mathematical Finance, Lunteren January 2019.
• Member of bilateral German-Slovak project ENANEFA (Efficient Numerical Approximation of Nonlinear Equations in Financial Applications), 01.2018 – 12.2019.