Many problems in science and engineering behave spatially or temporally as if there were multiple scales. Using ideas from machine learning, we probe for these scales by looking at the spectrum of diffusion operators on the data sets generated by these scientific problems. The resulting low-dimensional description allows us to do things like projective coarse integration, free energy calculations, bifurcation analysis, etc.

Abstract Type
poster
Primary Author
Benjamin Sonday
University
Princeton University
Abstract Title
Probing for “slow” variables using diffusion maps
Abstract Author(s)
Benjamin Sonday, Yannis Kevrekidis
Username
sonday
Fellowship Year
2006
First Name
Benjamin
Last Name
Sonday
Program
CSGF
Area of Study
Applied Mathematics
Poster Group
Group 1
Poster Number
5