How to include disorder in the numerical computation?

In summary, Monte Carlo algorithms are often used to incorporate randomness in calculations and modeling. This may be applicable to the need for 100 disorder realizations. Two questions arise: (1) Do different realizations require different random number generation seeds? (2) Is the simple average of all realizations the desired result?
  • #1
PRB147
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For example, in tight binding model, the site energy is random
I want to have 100 disorder realizations, then I have the following
two questions:

1. Do different realization needs different seed in the random number generation subroutine?

2. When all realizations are completed, the result should be the simple average? (i.e., [tex]\frac{\sum_{n=1}^{100} P_n}{100}[/tex].
)
Please help me, if you know, Thank you in advance!
 
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  • #2
Whenever one needs to include some "random" processes in a particular calculation or modeling, one tends to use some form of Monte Carlo algorithm. Not sure if this is applicable to you, but it is used a lot in different areas of physics.

Zz.
 
  • #3
Thanks you, Zz, you are right.
 

Related to How to include disorder in the numerical computation?

1. How can I incorporate disorder into my numerical computations?

Disorder can be included in numerical computations through the use of random number generators, statistical methods, and Monte Carlo simulations.

2. What is the significance of incorporating disorder in numerical computations?

Incorporating disorder allows for a more realistic and accurate representation of real-world systems, as many natural and physical systems exhibit inherent randomness and uncertainty.

3. Can disorder be accounted for in all types of numerical computations?

Yes, disorder can be incorporated into various types of numerical computations, such as simulations, optimization, and data analysis.

4. Are there any challenges associated with including disorder in numerical computations?

One challenge is determining the appropriate level of disorder to include in the computation, as too much or too little can lead to inaccurate results. Another challenge is the increased computational complexity and time required to account for disorder.

5. How can I validate the accuracy of my numerical computations with disorder?

One way to validate the accuracy is to compare the results with experimental or real-world data. Additionally, sensitivity analyses and testing different levels of disorder can help assess the robustness of the numerical computations.

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