3 points,
Fall Term 2011
Thursday, 3:20PM - 5:00PM
G63.2480.001:
ADVANCED TOPICS IN APPLIED MATHEMATICS
(Vorticity and Imcompressible Flows)
3 points,
Spring Term 2011
Thursday, 3:20PM - 5:00PM
3 points,
Spring Term 2010
Thursday, 3:20PM - 5:00PM
Grading: This course will be set up as a reading
course.
G63.2830.001
ADVANCED
TOPICS IN APPLIED MATHEMATICS (Vorticity and Incompressible Flow)
-
CLOSED FOR THE SEMESTER
3 points. Fall Term 2009
Thursday, 3:15PM - 5:00PM
Prerequisite: no
background besides some familiarity with elementary PDE is needed.
The course will cover material in chapters 1-5 and 7 of the text by
Majda and Bertozzi. If time permits, there will also be some
lectures on statistical theories for vortices and an introduction to
hurricane dynamics. The main goal is to introduce graduate
students, post docs, and visitors to the fascinating interplay
among exact solutions, nonlinear analysis, numerical computing, and
statistical ideas in developing intuition about fluid flow.
Grading: this course will be graded as a seminar course.
G63.2840.001:
ADVANCED TOPICS IN APPLIED MATHEMATICS (Fluctuation Dissipation
Theorems and Climate Change)
3 points,
Spring Term 2009
Thursday, 3:15PM - 5:00PM
Can one do climate change response by
computing suitable statistics of the present climate: This is an
applied challenge of obvious practical importance. This class focuses
on these issues from the viewpoint of modern applied mathematics, where
ideas from dynamical systems, statistical physics, information theory,
and stochastic-statistical dynamics will be blended with suitable
qualitative and quantitative models and novel numerical algorithms to
attach these questions.
The course has no formal requirements, but familiarity with elementary ODE and SDE is useful background. Chapters 2 and 3 of the book, "Information Theory and Stochastic for Multiscale Nonlinear Systems" by Majda, Abramov and Grote (American Mathematical Society) will provide the introductory material for these topics. Additional material, such as coping with model error and ensemble predictions, will also be discussed.
Grading: This course will be graded as a seminar course.