LECTURES

Chapter 15: Complexity

• Lecture 40: April 30
  • Forest fire automaton model
  • Neural networks and associative memory
• Lecture 39: April 27
  • Two-dimensional sandpile automaton
• Lecture 38: April 25
  • The Gutenberg-Richter Law
  • One-dimensional sandpile automaton
• Lecture 37: April 20
  • Lattice Gas Model of Fluid Flow
• Lecture 36: April 18
  • 2-D Cellular Automata (continued)
• Lecture 35: April 16
  • Conway's Game of Life
• Lecture 34: April 13
  • One-dimensional Boolean cellular automaton

Chapter 18: Quantum Systems

• Lecture 33: April 11
  • Path Integral Quantum Monte Carlo (continued)
• Lecture 32: April 9
  • Path Integral Quantum Monte Carlo
• Lecture 31: April 6
  • Quantum Systems - Catching up!
• Lecture 30: April 4
  • Random Walk Quantum Monte Carlo
• Lecture 29: April 2
  • Quantum Systems - Catching up!
  • Parabolic partial differential equations.
• Lecture 28: March 30
  • Quantum Systems - Catching up!
• Lecture 27: March 28
  • Time-Dependendent Schrödinger Equation
• Lecture 26: March 26
  • Bound state solutions by deterministic integration

Chapter 17: Monte Carlo Simulation of the Canonical Ensemble

• Lecture 25: March 23
  • Finite Size Scaling - Catching up!
• Lecture 24: March 21
  • Autocorrelations - Catching up!
• Lecture 23: March 19
  • Ising phase transition and finite size scaling
• Lecture 22: March 16
  • Autocorrelations in the 2-D Ising model simulation
• Lecture 21: March 12
  • Counting States: Energy versus Entropy
• Lecture 20: March 2
  • The Two-Dimensional Ising Model

Chapter 16: The Microcanonical Ensemble

• Lecture 19: February 28
  • One dimensional Ising Model: theory and simulation
• Lecture 18: February 26
  • The One-Dimensional Ising Model
• Lecture 17: February 23
  • The Microcanonical Ensemble

Chapter 12: Random Walks

• Lecture 16: February 21
  • Application of the pivot algorithm
• Lecture 15: February 19
  • Kink-jump polymer dynamics
• Lecture 14: February 16
  • Symmetries of the square lattice
  • The double cul-de-sac polymer
• Lecture 13: February 14
  • Reptation method for enriching a polymer sample
• Lecture 12: February 12
  • More on Self-Avoiding Random Walks
  • Applications of Random Walks to Polymers
  • DNA is a Polymer
• Lecture 11: February 9
  • Modified Random Walks: A Lattice Gas
  • Self-Avoiding Random Walk in 2 Dimensions
• Lecture 10: February 7
  • A random walk in two dimensions
  • More on graphics programming

Chapter 11: Numerical Integration and Monte Carlo Methods

• Lecture 9: February 5
  • Importance Sampling
  • Metropolis Monte Carlo Algorithm
• Lecture 8: February 2
  • Generating the Gaussian distribution
  • Overview of Java graphics programming
• Lecture 7: January 31
  • Monte Carlo Quadrature and Error Analysis
• Lecture 6: January 29
  • Quadrature and Integration
  • Programming the Rectangular Approximation
  • Multidimensional Quadrature

Chapter 7: Random Processes

• Lecture 5: January 26
  • Random Walks
  • 2-D Random Walk - Java Graphics
• Lecture 4: January 24
  • Binomial, Poisson, and Gaussian Distributions
  • Relationship between Poisson and Gaussian distributions
• Lecture 3: January 22
  • Darts and the Poisson distribution
  • Another Poisson applet
• Lecture 2: January 19
  • Random sequences
  • Pseudo-random number generators
  • How good are they?
• Lecture 1: January 17
  • Random processes
  • Tossing a coin
  • Random number generators