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Welcome‎ > ‎Courses‎ > ‎

Introduction to Statistical Physics from a Computational Perspective

Lesson 1: Introduction
Lesson 2: NetLogo
Lesson 3: Integrating Newton law of motion
Lesson 4: 1D Dynamical Systems
Lesson 5: 2D Dynamical Systems
Lesson 6: Low-dimensional chaos
Lesson 7: Random walk and stochastic processes
Lesson 8: Spatial stochastic systems, Markov processes and mean-field
Lesson 9: Thermodynamics
Lesson 10: The quantum world
Lesson 11: Statistical mechanics
Lesson 11: Phase transition, mean field, Monte Carlo computation
Lesson 12: Stochastic optimisation: simulated annealing, genetic algorithms, pruning and enriching
Lesson 13: Disordered systems, spin glasses, neural networks
Lesson 14: Kinetic theory, Boltzmann Equation, Boltzmann H theorem
Lesson 15: Complexity
Lesson 16: Networks, small world

Pagine secondarie (12): 1D dynamical systems 2D dynamical systems Integrating Newton Laws Introduction Low-dimensional chaos NetLogo Phase transition, mean field, Monte Carlo computation Random walk and stochastic processes Spatial stochastic systems, Markov processes and mean-field Statistical mechanics The quantum world Thermodynamics
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