Course description
The course will cover a suitable subset of the following topics. The final curriculum will be decided with the participants at the beginning of the course.
- Gaussian measures on Hilbert space
- Hilbert-space-valued Wiener processes and stochastic integration
- Existence and uniqueness of solutions to stochastic partial differential equations
- Strong and weak approximations of solutions with convergence analysis
- Simulation of Wiener processes
- Monte Carlo and multilevel Monte Carlo methods
Requirements and Selection
Entry requirements
Basic knowledge in numerical analysis, probability theory, partial differential equations, stochastic processes.
Selection
Not relevant
Course syllabus
NFMV019
Department
Department of Mathematical Sciences
Subject
Natural Science and Mathematics
Type of course
Subject area course
Keywords
Stochastic partial differential equations