Can anyone help with this integral?

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Discussion Overview

The discussion revolves around the evaluation of the integral \(\int_0^1 e^{ax^2} \operatorname{erf}(bx) dx\), where "erf" denotes the error function. Participants explore potential methods for solving this integral, including series expansions, changes of variables, and references to existing literature.

Discussion Character

  • Exploratory
  • Technical explanation
  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • One participant notes the difficulty in finding a closed form solution for the integral in standard references and software like Mathematica and Maple.
  • Another participant suggests that if \(a = b^2\), Mathematica provides a specific answer involving the Hypergeometric function.
  • Some participants express a desire for a closed form solution without the constraint of \(a\) being equal to \(b^2\).
  • A participant proposes defining a function \(\mathcal{P}(a, b)\) for the integral, implying that a closed form might exist but is not readily available.
  • There are suggestions to explore changes of variables or complex contour integrals, though one participant reports unsuccessful attempts with these methods.
  • A reference to a specific article is made, indicating that the integral resembles those discussed in the literature, which may provide insight into its evaluation.
  • Another participant introduces the "Cumulative Bivariate Normal Probability Function" and provides a formula that relates to the original integral, thanking another participant for their hint.
  • There is a question about the utility of the introduced function compared to previously suggested methods.

Areas of Agreement / Disagreement

Participants do not reach a consensus on the existence of a closed form solution for the integral. Multiple competing views and methods are presented, with some participants expressing skepticism about the integral's solvability in closed form.

Contextual Notes

Participants mention limitations in existing references and the potential for defining new functions to represent the integral. There is also discussion about the challenges of changing the limits of integration and the complexity of the integral itself.

ricardop
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Hello
I need to evaluate this integral:

[tex]\int_0^1e^{ax^2}erf(bx)dx[/tex]

where "erf" is the error function:[itex]erf(x)=\frac{2}{\sqrt{\pi}}\int_0^x\exp(-t^2)dt[/itex].
I can't find this integral in Gradshteyn, Abramowitz, Prudnikov, etc.
Neither Mathematica nor Maple do this integral (definite or indefinite).
The best I can do is by series...
Can anyone help with this integral?

Thanks
 
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ricardop said:
I can't find this integral in Gradshteyn, Abramowitz, Prudnikov, etc.
Neither Mathematica nor Maple do this integral (definite or indefinite).

So it is very likely that there is no closed form solution. So what kind of an answer would you like then?
 
If a happens to be equal b^2, then Mathematica gives the answer

[tex] \frac{b}{\sqrt{\pi}}\operatorname{HypergeometricPFQ}[\{1,1\},\{\frac{3}{2},2\},b^2][/tex]
 
I'm looking for a closed form... with a not necessarily equal b.
 
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Pere Callahan said:
If a happens to be equal b^2, then Mathematica gives the answer

[tex] \frac{b}{\sqrt{\pi}}\operatorname{HypergeometricPFQ}[\{1,1\},\{\frac{3}{2},2\},b^2][/tex]

Yes, I knew it.
This is in Prudnikov tables.
But a not necessarily equal b.
Thanks
 
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CompuChip said:
So it is very likely that there is no closed form solution. So what kind of an answer would you like then?

I'm looking for a closed form.
Thanks
 
So if you checked several references an none of them gives one, maybe it doesn't exist :)
You can define it, though.
RicardoP's function
[tex]\mathcal P(a, b) := <br /> \int_0^1e^{ax^2} \operatorname{erf}(bx)dx[/tex]
Then the closed form solution is
[tex]\mathcal P(a, b)[/tex]
 
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You may be able to do a change of variables to make the limits from 0 to infinity, then try some sort of complex contour integral. I haven't tried this yet but its worth a shot.
 
maze said:
You may be able to do a change of variables to make the limits from 0 to infinity, then try some sort of complex contour integral. I haven't tried this yet but its worth a shot.

I can change x=1/y, and the interval goes from 1 to \infty, but not from 0 to \infty, or -\infty to \infty.

I tryed several changes of variable...
I changed the order of integration (this is a double integral).
I tryed complex contour integral too, but without sucess.

Thanks!
 
  • #10
CompuChip said:
So if you checked several references an none of them gives one, maybe it doesn't exist :)

This integral is very alike the integrals in the article:
"Integration of a Class of Transcendental Liouvillian Functions with Error-Functions, Part II."
Paul H. Knowles, J. Symb. Comp. 16 (1993) 227.
http://dx.doi.org/10.1006/jsco.1993.1042"
I'm working on it...
 
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  • #11
The "Cumulative Bivariate Normal Probability Function" is defined by:
[tex]L(h,k,\rho)=\int_h^\infty dx\int_k^\infty dy\;g(x,y,\rho)[/tex]
where
[tex]g(x,y,\rho)=\frac{1}{\sqrt{1-\rho^2}}Z(x)Z\left(\frac{y-\rho x}{\sqrt{1-\rho^2}}\right)[/tex]
is the "Bivariate Normal Probability Function" and
[tex]Z(x)=\frac{\exp(\frac{-x^2}{2})}{\sqrt{2\pi}}[/tex]
is the "Normal Probability Function", in Abramowitz & Stegun's notation.

With these definitions we have the answer
[tex]\int_0^1 dx\exp(-ax^2)\mathrm{erf}(bx)=\frac{1}{2}\sqrt{\frac{\pi}{a}}\mathrm{erf}(\sqrt{a})-\frac{1}{\sqrt{a\pi}}\arctan\left(\frac{\sqrt{a}}{b}\right)+2\sqrt{\frac{\pi}{a}}L\left(\sqrt{2a},0,\frac{-b}{\sqrt{a+b^2}}\right)[/tex]

I want to thank Axel for the hint
http://groups.google.com.au/group/sci.math.symbolic/browse_thread/thread/ddbbb1f3ab27b3c4"
 
Last edited by a moderator:
  • #12
Is this any more useful than using RicardoP's function as suggested by CompuChip?
Granted, you can now make use of some known properties of this "Cumulative Bivariate Normal Probability Function"...
 
  • #13
Pere Callahan said:
Is this any more useful than using RicardoP's function as suggested by CompuChip?
Granted, you can now make use of some known properties of this "Cumulative Bivariate Normal Probability Function"...

obvious...
 

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