How to solve an integral with the Inverse error function

In summary, the problem is very complicated and the individual is looking for an analytical approximation for the integral. However, he or she is not sure how to do it.
  • #1
matteo86bo
60
0
Hi,
this is not a homework and my problem is much bigger for me to give full details here. I came across this integral

[itex]\mathcal{I}(\xi)=\int^{\xi_c}_{\xi}{\rm d}\xi^\prime\exp\left[\sqrt{2}\sigma\,{\rm Erf}^{-1}\left(1-\frac{8\pi}{3}{\xi^\prime}^3\right)\right][/itex]


where Erf[itex]^{-1}[/itex] is the inverse error function and

[itex]\xi_c=\left[\frac{3}{8\pi}\left(1-{\rm Erf}\left(\frac{\sigma^2-\sqrt{2}\sigma\,{\rm Erf^{-1}(2\beta-1)}}{\sqrt{2}\sigma}\right)\right)\right]^{1/3}[/itex]

with [itex]0\le\beta\le1[/itex].

I would like get an analytical approximation but I can't figure out a way to do that, even with software like Mathematica. I tried solving the integral numerically and I find a reliable solution, however, I'm mostly interested in points where [itex]\xi\to\xi_c^-[/itex], and here the inverse error function diverges.

Do you have any ideas on how to approximate this integral?
 
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  • #2
Use a taylor series?
 
  • #3
Thank you for your answer. But I don't understand how I can Taylor expand where the integrand diverges.
Also, do you see a way to normalize the integral and/or make it more simple to solve it numerically.
What I find challenging is that for every value of beta I have to numerically solve the integral. It would be better to normalize somehow the result with beta and then do the integral only once.
 
  • #4
The inverse errorfunction can be defined as a Maclaurin Series.
That seems like a way to get rid of some awkwardness there, but I don't know for sure that it is the best way to go.

But I don't immediately see how to make it much simpler besides that.
 
  • #5
Thanks, the problem seems very complicated and I think I have to resort to a numerical integration.
However, that would be easier if one could write

[itex]\mathcal{I}(\xi,\beta)=f(\beta)\mathcal{I}^{\prime}(\xi)[/itex]

so this way I would have to integrate only once. Do you see a way to possibly achieve that?
 
  • #6
I understand this is quite an old post but: any luck with computing this integral? I have come across something similar recently.
 

Related to How to solve an integral with the Inverse error function

1. How do you use the Inverse error function to solve an integral?

The Inverse error function, also known as the inverse Gaussian function, is a mathematical function that can be used to solve integrals involving the error function. To use it, you first need to express the integral in terms of the error function, then use the inverse error function to solve for the variable in the integral.

2. What is the formula for the Inverse error function?

The formula for the Inverse error function is:

erf-1(x) = √(2) * erfi-1(2x)

where erf-1(x) is the Inverse error function, and erfi-1(x) is the inverse imaginary error function.

3. Can the Inverse error function be evaluated numerically?

Yes, the Inverse error function can be evaluated numerically using various methods such as Newton's method or the bisection method. However, these methods may not be efficient for very large or very small values of x, in which case special algorithms may need to be used.

4. How is the Inverse error function related to the complementary error function?

The Inverse error function is closely related to the complementary error function, as they are inverse functions of each other. The complementary error function is defined as 1 - erf(x), and the inverse of this function is the inverse complementary error function, which is equal to 1 - erf-1(x).

5. What are some real-world applications of using the Inverse error function to solve integrals?

The Inverse error function has various applications in fields such as statistics, physics, and engineering. It is commonly used to solve integrals involving the normal distribution, which is a common probability distribution in statistics. It can also be used to solve problems related to heat transfer, diffusion processes, and signal processing.

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