Monte Carlo Simulation

Background

Monte Carlo Simulations are statistical simulation methods where statistical simulation is defined as any method that utilizes sequences of random numbers to perform the simulation.

Although Monte Carlo Simulation has its roots in the gambling casinos of Monaco, it is now regularly used in many diverse applications, from the simulation of complex physical phenomena such as radiation transport in the earth's atmosphere and the simulation of the esoteric subnuclear processes in high energy physics experiments, to the simulation of something as simple as a Bingo game.

The goal of the Monte Carlo method is to simulate the physical (or mathematical) system by random sampling from these PDFs and by performing the necessary supplementary computations needed to describe the system evolution.

Applications of Monte Carlo Simulations

Although by no means complete, the following list of Monte Carlo examples illustrate the diversity of the types of applications that have been addressed using statistical simulation techniques.

• Nuclear reactor design
  
• Quantum chromodynamics
  
• Radiation cancer therapy
  
• Traffic flow
  
• Stellare evolution
  
• Econometrics
  
• Dow Jones forecasting
  
• Oil well exploration
  
• VLSI design

Programming a Monte Carlo Simulation in Origin C

Origin C allows you to program simulations using the Monte Carlo methods in Origin. Three sample projects are supplied with Origin. They are:

• Ising Model
  
• Random Walk
  
• Value of Pi
  

Much of the information you see here has been derived or taken directly from the following web site: http://csep1.phy.ornl.gov/mc/mc.html

The CSEP is sponsored by the US Department of Energy and has the following CSEP copyrights: Copyright (C) 1991, 1992, 1993, 1994, 1995, 1996