Machine Learning Methods for Solving Partial Differential Equations
Event details
| Date | 04.08.2023 |
| Hour | 10:00 › 12:00 |
| Speaker | Anand George |
| Location | |
| Category | Conferences - Seminars |
EDIC candidacy exam
Exam president: Prof. Rüdiger Urbanke
Thesis advisor: Prof. Nicolas Macris
Co-examiner: Prof. Olivier Lévêque
Abstract
coming soon
Background papers
1) Stochastic Controls - Hamiltonian Systems and HJB Equations, In Chapter 7, Sections 1, 2 and 3.1 (pages 345-360) https://link.springer.com/book/10.1007/978-1-4612-1466-3
2) Algorithms for solving high dimensional PDEs: from nonlinear Monte Carlo to machine learning https://iopscience.iop.org/article/10.1088/1361-6544/ac337f
3) Random feature neural networks learn Black-Scholes type PDEs without curse of dimensionality https://arxiv.org/abs/2106.08900
Exam president: Prof. Rüdiger Urbanke
Thesis advisor: Prof. Nicolas Macris
Co-examiner: Prof. Olivier Lévêque
Abstract
coming soon
Background papers
1) Stochastic Controls - Hamiltonian Systems and HJB Equations, In Chapter 7, Sections 1, 2 and 3.1 (pages 345-360) https://link.springer.com/book/10.1007/978-1-4612-1466-3
2) Algorithms for solving high dimensional PDEs: from nonlinear Monte Carlo to machine learning https://iopscience.iop.org/article/10.1088/1361-6544/ac337f
3) Random feature neural networks learn Black-Scholes type PDEs without curse of dimensionality https://arxiv.org/abs/2106.08900
Practical information
- General public
- Free
Contact
- edic@epfl.ch