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Monte Carlo Modeling and Simulation

Monte Carlo M&S is the one which uses a model built based on statistical random sampling. The model typically does not represent time-varying relationships. Monte Carlo M&S is commonly used in disciplines such as Chemistry, Computational Engineering, Financial Probabilistic Modeling, Mathematics, Nuclear Engineering, (Computational, Nuclear, Statistical) Physics, and Reliability Engineering.

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Gas Cloud Simulation

A simulation of how a gas cloud that has been observed approaching the supermassive black hole at the centre of the galaxy.

Advanced Test Reactor Simulation

Advanced simulation capabilities can model reactors, such as INL's Advanced Test Reactor shown here, from atomic scales to full-sized reactor assemblies.

Underwater EVA Simulation

Underwater simulation of extravehicular activity (EVA).

Combat Simulation

A screen capture depicts a combat situation in a digital world during a command post exercise inside the Joint Multinational Simulations Center.

Motorcycle Simulator

Testing motorcycle simulator at the Pentagon to get a feel for how the bike would handle some situations.

Wake Turbulence Simulation

Wake turbulence simulation for an Airbus A340 on final approach, just before touchdown.

Driving Conditions Simulator

View from the cockpit of the simulator. Multiple screens provide a view similar to what one would see in a snowplow on the road.

Molecules Simulation

Dynamic computer simulations of molecular systems.

Global Aerosols Simulation

This portrait of global aerosols was produced by a Goddard Earth Observing System Model, Version 5 (GEOS-5) simulation at a 10-kilometer resolution.

Driving Simulation

Simulation for driving training.

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Special Interest Group (SIG) on SImulation and Modeling (SIM)
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Monte Carlo Modeling and Simulation





Disciplines Using Monte Carlo M&S

    • Biology
    • Chemistry
    • Computational Engineering
    • Financial Probabilistic Modeling
    • Mathematics
    • Nuclear Engineering
    • Physics (Computational, Nuclear, Statistical)
    • Reliability Engineering
    • Statistics

Intended Uses / Objectives

    • Problem Solving based on statistical random sampling


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