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 da considerare: Controllo e sincronizzazione; Boltzmann machines, Neural networks, branching processes (e funzioni generatrici), equazioni differenziali stocastiche (es. Ising. percolazione diretta), MArkov processes, master equation, ecc. |
Introduction to Statistical Physics from a Computational Perspective
Pagine secondarie (12):
1D dynamical systems
2D dynamical systems
Integrating Newton Laws
Introduction
Low-dimensional chaos
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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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