Instructor: Fei Lu
Class meets: TTh, 910:15, Garland 97
Office Hours: TTh 10:1511:15, Krieger 301
Webpage:
http://www.math.jhu.edu/~feilu/19Spring/StoDS/stoDS19.html
Email:
feilu## ( ## = @math.jhu.edu)
Textbook: Rafail Khasminskii: Stochastic stability
of Differential equations
Reference books/readings:
Course plan (tentative): The course will present a introduction to stochastic dynamical systems and some applications in model reduction and data assimilation. The main focus will be on stability and ergodicity of stochastic dynamical systems, including stochastic differential equations driven by white noise and fractional noise, and their numerical approximations. We will then discuss applications of stochastic dynamical systems, related topics include inference, model reduction and data assimilation. The course is open to majors in math, applied math, statistics, engineering. I will try to accommodate the backgrounds of students.
Prerequisite: analysis (real analysis, functional
analysis); probability; differential equations (preferably
stochastic differential equations).
Grading: Grade will be based on homework assignments and
presentations. There is no exam.
Stochastic Grading: Your grade will be based on presentation of homework exercises. Each week, there will be 13 exercises assigned. One student will be randomly picked to present his/her solution to a randomly picked excercise in class in 35 minutes. The audience can ask questions to help identifying the gaps in the presenter's proof. The presenter gets 23 points, the audience who helped identifying a gap gets 1 point. We will randomly pick one student to type up the solution, who will get 1 point. In the end, grade will be assigned based on statistics. Above mean: A; [mean  2 std, mean]: B; else, C.
week  Topics  Sections  Other 

1/29, 1/31  Chp1 

2/5, 2/7  Chp2  
2/12, 2/14  Chp3  
2/19, 2/21  Chp3  
2/26, 2/28  Chp4  
3/5, 3/7  Chp4  
3/12, 3/14  Stochastic Approximation:  Chp7.5 RobbinsMonro51: A stochastic approximation method Bottou et al18: Optimization Methods for LargeScale Machine Learning CuckerSmale01: On the mathematical foundations of learning 

3/19, 3/21  spring break 

3/26, 3/28  SDE with fBm  Hairer05:
Ergodicity of stochastic differential equations driven by
fractional Brownian motion
HO07: Ergodic theory for SDEs with extrinsic memory 

4/2, 4/4  
Class on 4/2 cancelled; to be madeup on May 7 and 9th 

4/9, 4/11  measure preserving DS 
Arnold: appendix  
4/16, 4/18  Random and stochastic DS 
Arnold: chapter 12, HO07  
4/23, 4/25  MoriZwanzig formalism 
ChorinHald (2013) Chapter 9;
Slides of Li (2019) HudsonLi (2018)  
4/30, 5/2  Gradient systems and Hamiltonian systems 


5/7, 5/9  Inference of SDE 
