Monte Carlo Simulations in Statistical Physics — From Basic Principles to Advanced Applications

2012 ◽  
pp. 93-166 ◽  
Author(s):  
Wolfhard Janke
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Statistical Physics ◽  
Basic Principles ◽  
Advanced Applications

A new approach to Monte Carlo simulations in statistical physics: Wang-Landau sampling

2004 ◽  
Vol 72 (10) ◽  
pp. 1294-1302 ◽  
Author(s):  
D. P. Landau ◽  
Shan-Ho Tsai ◽  
M. Exler
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Statistical Physics ◽  
New Approach

Acceleration Algorithms in Monte Carlo Simulations in Statistical Physics

1991 ◽  
Vol 02 (01) ◽  
pp. 201-208
Author(s):  
ROBERT H. SWENDSEN
Keyword(s):  
Monte Carlo ◽  
Phase Transitions ◽  
Monte Carlo Simulations ◽  
Statistical Physics ◽  
Relaxation Times ◽  
Critical Slowing Down ◽  
Cluster Approach ◽  
New Developments ◽  
Slowing Down ◽  
Thermodynamic Phase

Monte Carlo simulations of thermodynamic phase transitions are usually hampered by long relaxation times due to the phenomenon of “critical slowing down.” Using a mapping due to Fortuin and Kasteleyn, a cluster approach to Monte Carlo simulations has been developed, which greatly reduces relaxation times, improving efficiency by up to two or three orders of magnitude. New developments and extensions of this approach are also discussed.

HOW MONTE CARLO SIMULATIONS CAN CLARIFY COMPLEX PROBLEMS IN STATISTICAL PHYSICS

2001 ◽  
Vol 15 (09) ◽  
pp. 1193-1211 ◽  
Author(s):  
KURT BINDER
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Statistical Physics ◽  
Condensed Matter ◽  
Finite Size ◽  
Two Dimensional ◽  
Finite Size Scaling ◽  
Complex Phase ◽  
Finite Systems ◽  
Scaling Methods

Statistical mechanics of condensed matter systems in physics (fluids and solids) derives macroscopic equilibrium properties of these systems as averages computed from a Hamiltonian that describes the atomistic interactions in the system. While analytic methods for most problems involve uncontrolled approximations, Monte Carlo simulations allow numerically exact treatments, apart from statistical errors and from the systematic problem that finite systems are treated rather than the thermodynamic limit. However, this problem can be overcome by finite size scaling methods, and thus Monte Carlo methods have become a very powerful tool to study even complex phase transitions. Examples given will include unmixing of polymer blends, two-dimensional melting, etc.

A parallel multiprocessor system for Monte Carlo simulations in statistical physics

1989 ◽  
Vol 60 (9) ◽  
pp. 2981-2991 ◽  
Author(s):  
Jukka Saarinen ◽  
Kimmo Kaski ◽  
Jouko Viitanen
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Statistical Physics ◽  
Multiprocessor System
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