SPECIAL TOPICS - Data Assimilation in Dynamical Systems
Title: Data Assimilation in Dynamical Systems
* Dynamical systems: unstable manifolds and attractors, Lyapunov
exponents, sensitivity to initial conditions and concept of
* Data assimilation and filtering theory: Bayesian viewpoint
* Nonlinear Filtering: Particle filtering and sampling methods
* Advanced topics: parameter estimation, Lagrangian data assimilation
Prerequisites: The course is intended for graduate students in statistics, mathematics or other mathematically related disciplines. Students in fields such as atmospheric and oceanic sciences are welcome to attend provided they have exposure to mathematical courses at an appropriate level, such as a standard undergraduate calculus, a course in linear algebra and some course in probability or statistics.
Assessment: Each student will be expected to conduct a small project developed in consultation with the instructors. Depending on the number of students in the course, students may be grouped into teams of two or three students. Each student or team will present their findings at the final class sessions, and should also prepare a short written report.