Uncertainties of numerical results

In summary, people typically determine uncertainties on results from Monte Carlo simulations by repeating the simulation multiple times and taking the standard deviation of the results. If the simulation is too computationally expensive, they may repeat it for a few low initial samples and extrapolate the uncertainty using a curve fit, with 1/sqrt(N) as a possible function.
  • #1
Niles
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How do people generally determine uncertainties on results that are based on Monte Carlo simulations? Take this fictive example, just so we have something specific to talk about:

I look at 106 particles, confined to a box. There is a small hole in one of the walls, and at some time t0 I am interested in knowing how many particles N have diffused out of the box. This can be simulated by a Monte Carlo approach (Brownian motion).

This number N will vary each time I perform the simulation, but it will converge the larger I make the initial sample. Nonetheless, I guess an uncertainty is still present - how can we determine that in general?

Thanks for input in advance.
 
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  • #2
I'm not sure if there's a more elegant method, but I'd repeat the simulation multiple times and take the standard deviation of the results. Perhaps if it's too computationally expensive to do this with a very high N, you can repeat it for a few low N and extrapolate the uncertainty as a function of N using a curve fit? I assume it would be 1/sqrt(N)?
 

1. What is an uncertainty of a numerical result?

An uncertainty of a numerical result is a measure of the possible range of values that the result could have taken due to errors or limitations in the measurement process. It is often expressed as a margin of error or a confidence interval.

2. How is uncertainty calculated?

Uncertainty is typically calculated by analyzing the sources of error in a measurement and determining their potential impact on the final result. This can involve statistical techniques such as standard deviation or propagation of error formulas.

3. Why is it important to consider uncertainties in numerical results?

Considering uncertainties in numerical results is important because it provides a more accurate and comprehensive understanding of the data. It allows for a more realistic interpretation of the results and helps to identify potential sources of error or areas for improvement in the measurement process.

4. How can uncertainty affect the validity of a scientific study?

Uncertainty can significantly impact the validity of a scientific study if it is not properly accounted for. If uncertainties are not considered, the results may be misleading or incorrect, leading to inaccurate conclusions. This can also affect the reproducibility and reliability of the study.

5. How can uncertainties be reduced in numerical results?

Uncertainties can be reduced by improving the measurement process, using more accurate and precise instruments, and increasing the sample size. Additionally, conducting multiple trials and averaging the results can help to minimize uncertainties and increase the reliability of the data.

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