Computational Methods for Nonlinear Systems
Physics 7682 / Computing & Information Sciences 6229 - Fall 2009
Mondays & Fridays 1:30-3:30, Rockefeller B3
(directions)
http://www.physics.cornell.edu/~myers/teaching/ComputationalMethods/
Computational science and engineering involves the synthesis of data
structures, algorithms, numerical analysis, programming methodologies,
simulation, visualization, data analysis, and performance
optimization, all applied to the study of complex problems in science
and engineering. Physics 7682 / CIS 6229 is a graduate computational
science laboratory course, emphasizing hands-on programming to address
a number of interesting problems arising in physics, biology,
engineering, applied mathematics, and computer science. The course is
largely self-paced, allowing students to choose from among a variety
of topics, and explore new problems of particular interest.
Topics
Course modules are drawn from a number of different fields, reflecting
the course's role as a core element in the curriculum of Cornell's
IGERT Program in Nonlinear Systems, which is organized broadly around
the themes of complex networks, biolocomotion and manipulation, gene
regulation, and pattern formation. Modules are designed to expose
students to techniques and methods from a variety of disciplines, not
normally encompassed in a single course. Computational methods
include solution of ordinary and partial differential equations,
graph traversal, Monte Carlo, search trees,
and various techniques in data analysis.
- Complex networks, small worlds, and percolation
- Human locomotion and models of walking
- Dynamical systems, chaos, and iterated maps
- Pattern formation and spiral waves in cardiac tissue
- Chemical kinetics and gene regulatory networks
- Random matrix theory
- Random walks, extremal statistics, and stock fluctuations
- Lattice Monte Carlo and the Ising model
- Satisfiability and phase transitions in NP-complete problems
- Molecular dynamics and the emergence of thermodynamics
Texts & Articles
Information and Links
Python resources
Local Python material
Web resources for Python
