Stochastic simulation methods
- Course code: 11306
- Semester: 1st
- Credits (ECTS): 6 credits
- Teaching hours:
- Teaching guide: Teaching Guide (PDF)
- Monte Carlo integration: Problems in one variable. Statistical errors. Generation of random numbers.
- Monte Carlo integration in many variables: Metropolis and thermal bath.
- Collective algorithms for Ising and Potts models.
- Extrapolation techniques: Ferrenberg-Swendsen algorithm.
- Applications to phase transitions: critical phenomena, analysis in terms of finite size scaling.
- Main algorithms for the integration of stochastic differential equations: Euler, Milshtein and Heun methods.
- Numerical integration of partial differential equations: finite differences, pseudospectral methods, stochastic equations.
- Molecular dynamics. Temporal reversibility and symplectic algorithms. Hybrid Monte Carlo.
- Numerical simulation of master equations. First reaction and residence time algorithms.