Convergence analysis of probabilistic schemes for nonlinear partial differential equations

This project studies the Deep Second-order Backward Stochastic Equations (2BSDE) solver for fully nonlinear partial differential equations (PDE). Several improvements to the Deep BSDE solver proposed in the recent years are discussed. Results produced by [8] are served as the benchmark for this proj...

وصف كامل

محفوظ في:
التفاصيل البيبلوغرافية
المؤلف الرئيسي: Lim, Li Xiang
مؤلفون آخرون: Nicolas Privault
التنسيق: Final Year Project
اللغة:English
منشور في: Nanyang Technological University 2025
الموضوعات:
الوصول للمادة أونلاين:https://hdl.handle.net/10356/184479
الوسوم: إضافة وسم
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المؤسسة: Nanyang Technological University
اللغة: English
الوصف
الملخص:This project studies the Deep Second-order Backward Stochastic Equations (2BSDE) solver for fully nonlinear partial differential equations (PDE). Several improvements to the Deep BSDE solver proposed in the recent years are discussed. Results produced by [8] are served as the benchmark for this project. Two new ideas are proposed to improve the Deep 2BSDE solver - via modifying the objective function and incorporating an optimistic initialization of the neural networks. Numerical experiments are performed with PDEs in 1- and 5-dimensional settings to demonstrate the performance improvement of the proposed ideas. Finally, future work that can be done to extend the scope of this project is discussed.