Yes, I did manage to understand it from this reference
Viktor Grigoryan, "Partial Differential Equations" . I also implemented a
simple homogeneous variant of it in this PR. By the way, I also tried to
factor out the common code in a separate utils.py.

https://github.com/sympy/sympy/pull/1970

It would be really nice if you could review it or comment whenever you get
the time.


On Sun, Apr 7, 2013 at 5:27 AM, Aaron Meurer <[email protected]> wrote:

> Did you solve this? Looking back at the book I used when I took PDEs
> (Zauderer) I think you are talking about the method of
> characteristics. It looks like the solution should be
>
> dx/ds = a, dx/ds = b, dF/ds = c (note that the right-hand side can
> actually be c*F + d, in which case the third equation is dF/ds = c*F +
> d). Here we consider x and y to be functions of the new parameter s.
> There is also some work to invert the solution to solve for s and
> another parameter tau. See page 66-67 of Zauderer ("Partial
> Differential Equations of Applied Mathematics"), or some similar
> reference (p.s., I mention this book because it's the one that I used,
> but I don't actually recommend it as it is chalk full of typos as I
> recall).
>
> Aaron Meurer
>
> On Wed, Apr 3, 2013 at 9:16 AM, Manoj Kumar
> <[email protected]> wrote:
> > I'm sorry if it was ambiguous. I just saw something similar to that in
> the
> > paper I had mentioned. My interpretation most probably is completely
> wrong,
> > However I do seemed to have figured out the way it is supposed to be done
> > with the help of Google.
> >
> > Cheers
> > --
> > Regards,
> > Manoj Kumar,
> > Mech Undergrad.
> > BPGC
> > Blog
> >
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-- 
Regards,
Manoj Kumar,
Mech Undergrad.
BPGC
Blog <http://manojbits.wordpress.com>

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