I'm carrying out an experiment in which I have to solve the differential equations:


I don't know if one can do it by integration (I couldn't), but Wolfram Alpha doesn't come up with any solution. I can solve it numerically. I have some data $\{(x,y)\}$ and I would like to fit the equation $x(y)$ to get the constant $A$. I already know this constant experimentally, but I'd like to get a computational value for it. That's why I need the analytical solution. Thanks for your help

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    $\begingroup$ Not to say there isn't possibly an analytical solution. But if WolframAlpha can't find one, then it is unlikely to exist. $\endgroup$ – Simon S May 17 '15 at 16:24
  • $\begingroup$ If only there was the term $(y-l)$ in the numerator, then the integration would be straightforward by reversing the chain rule, leading to $x=\sqrt{1-A^2(ly-\frac{y^2}{2})}+C$. But, in its present form, it doesn't seem like an analytical solution is possible. $\endgroup$ – Alijah Ahmed May 17 '15 at 16:32
  • $\begingroup$ @Vladimir Vargas: In the equation, they are two parameters $A$ and $l$. Are they both unknown ? I mean : Is a fitting problem of non-linear regression for one or two parameters ? $\endgroup$ – JJacquelin May 9 '17 at 6:13
  • $\begingroup$ @Vladimir Vargas: Could you edit an example of data ${(x,y)}$ in order to test a method of regression. $\endgroup$ – JJacquelin May 9 '17 at 9:11

Analytical solving is possible. The result can be expressed on the form of the inverse function $x(y)$. The resulting equation involves elliptic integrals of the first and second kind (see below). That is complicated and moreover, the equation cannot be inverted on a closed form for $y(x)$.

So, I think that the analytic solution is of no help to fit the equation $x(y)$ to the data {(x,y)}. The fitting might be possible thank to a method of nonlinear regression.

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  • $\begingroup$ Wow. I have to look at the answer carefully and also remember the problem that I wanted to solve. Thanks for your effort $\endgroup$ – Vladimir Vargas May 10 '17 at 1:06

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