Illinois Institute of Technology, Chicago, August 2010

Stochastic partial differential equations are appropriate models for many randomly influenced systems. In order to quantify uncertainty and estimate predictability of these systems, numerical approximations of stochastic partial differential equations are crucial.

With Peter E. Kloeden as the Principal Lecturer, this conference will focus on new advances and possible future research directions in numerical approaches for simulating and analyzing stochastic partial differential equations, with applications for understanding uncertainty quantification, noise-induced phenomena and predictability.

Agency
National Science Foundation (NSF)
Institute
Division of Mathematical Sciences (DMS)
Type
Standard Grant (Standard)
Application #
0938235
Program Officer
Dean M Evasius
Project Start
Project End
Budget Start
2010-01-01
Budget End
2010-12-31
Support Year
Fiscal Year
2009
Total Cost
$34,975
Indirect Cost
Name
Illinois Institute of Technology
Department
Type
DUNS #
City
Chicago
State
IL
Country
United States
Zip Code
60616