Hello,

Thank you for your insights on SymPy's priorities. Following your
discussion about PDE solver, I'd like to inquire about SymPy's current
direction regarding *Computational Group Theory.*

I'm considering a project proposal focused on *implementing Quotient
Groups, Automorphism Groups, and algorithms for Infinite Groups*. Could you
please guide *if this aligns* with SymPy's *current priorities* and whether
*mentors* would be *available *for such a project?

I've taken relevant Abstract Algebra ( Group,Ring, Field, Galois Theory)
courses at university, and have started with the reference mentioned on the
ideas page :  *Handbook of Computational Group Theory*
Before proceeding further, I wanted to confirm if this area would be
considered of any value and relevance for SymPy's development at this time.

Best regards,
Ashutosh Rajora


On Thu, Feb 27, 2025 at 2:11 PM Aaron Meurer <[email protected]> wrote:

> I would also argue that symbolic PDE solvers are relevant and in-scope
> for SymPy. However, I also do agree with Oscar that as far as
> priorities go, many other things such as polynomials and matrices are
> much more important, as they affect virtually all parts of SymPy,
> whereas a PDE solver is not going to be used by any other part of
> SymPy.
>
> Aaron Meurer
>
> On Fri, Feb 7, 2025 at 7:44 AM Nicolas Guarin <[email protected]>
> wrote:
> >
> > Hello,
> >
> > I am in a different position than Oscar. Differential equations (and
> PDEs) have a place in the symbolic world. And the solution of them is one
> part. SymPy is far from doing what is possible with Maple regarding PDE
> solutions, for example. We are also lacking approximation done
> symbolically, as well. Asymptotic approximations, for example.
> >
> > I have used this kind of thing for benchmarking my numerical solutions
> using finite element methods, and consider that they are helpful.
> >
> > I have never been a mentor in GSOC in the past, but maybe I could try to
> if someone gives me a hand.
> >
> > Best regards,
> > Nicolás
> >
> >
> > On Thursday, February 6, 2025 at 10:07:44 AM UTC-5 [email protected]
> wrote:
> >>
> >> Hi Oscar,
> >>
> >>
> >> Thank you for your detailed response. I understand your perspective on
> prioritizing core functionalities. It makes sense that improving algebra,
> polynomials, matrices, and core solvers would provide a stronger foundation
> for symbolic computations.
> >>
> >> I appreciate your insights and keep these priorities in mind while
> considering future contributions to SymPy. While waiting for more reviews,
> I’ve begun looking into other core functionalities within SymPy that could
> be enhanced to improve the library further and make it a better fit for the
> project.
> >>
> >>
> >> Best regards,
> >>
> >> Jatin
> >>
> >> On Thursday, 6 February 2025 at 03:12:17 UTC+5:30 Oscar wrote:
> >>>
> >>> On Wed, 5 Feb 2025 at 13:41, Jatin Bhardwaj <[email protected]>
> wrote:
> >>> >
> >>> > Hello SymPy developers,
> >>> >
> >>> >
> >>> > I am interested in improving SymPy's PDE-solving functionality,
> particularly for quasilinear first-order PDEs, general first-order
> nonlinear PDEs, and second-order PDEs. Currently, SymPy has strong support
> for linear PDEs, but handling nonlinear cases—especially quasilinear and
> fully nonlinear PDEs—remains limited.
> >>> >
> >>> >
> >>> > My primary question is: Does expanding PDE support in these areas
> align with SymPy’s current development roadmap and priorities?
> >>> >
> >>> >
> >>> > If this aligns with SymPy’s goals, I would be enthusiastic about
> contributing to this effort. I have a strong foundation in calculus and
> differential equations, which I believe will be valuable in tackling this
> challenge. I’m prepared to delve into the computational implementation of
> these features and develop a concrete plan of action.
> >>> >
> >>> > To facilitate this process, I would greatly appreciate any guidance
> on:
> >>> > 1. Recommended resources for proposed features.
> >>> > 2. Any potential challenges or considerations unique to implementing
> nonlinear PDE solvers.
> >>>
> >>> Hi Jatin,
> >>>
> >>> SymPy does not have a broadly agreed development roadmap and list of
> >>> priorities. Rather different people have different things that they
> >>> are working on and would prioritise. I will answer in terms of my own
> >>> sense of a roadmap and priorities.
> >>>
> >>> SymPy has various solver functions e.g. solve for algebraic equations,
> >>> dsolve for ODEs and pdsolve for PDEs and many more. The usefulness of
> >>> these functions is often questionable. Even in the case of solve it
> >>> would often be better to use something else such as to compute
> >>> numerical solutions rather than analytic solutions. For dsolve, only
> >>> quite simple ODEs can be solved. The implementation can be improved to
> >>> handle more DEs but there would still be a tiny subset of problems
> >>> where analytic solutions can be computed and a vast array of practical
> >>> problems that realistically can only be handled numerically. When you
> >>> go to pdsolve and PDEs the set of cases that can be solved
> >>> analytically is so small that a function like pdsolve is almost
> >>> useless. It would be much more useful to users if SymPy just provided
> >>> something like an ndsolve function that could solve differential
> >>> equations numerically using SciPy's solvers rather than making them go
> >>> through lambdify.
> >>>
> >>> That does not mean that we can't do useful things with symbolics when
> >>> solving these different types of equations. Often though the useful
> >>> thing is to do some symbolic manipulation that then helps with a
> >>> subsequent approximate or numeric calculation e.g. we could compute a
> >>> series solution or transform the equations somehow. Some symbolic
> >>> manipulation is needed even just to set up a numeric solution to a PDE
> >>> so you could imagine something useful where SymPy can do that and then
> >>> set things up so that the problem could be solved numerically with
> >>> e.g. FEniCS.
> >>>
> >>> All of these are also things that could be built on top of SymPy
> >>> though so e.g. someone could make a library that depends on SymPy and
> >>> that uses it to do useful things with PDEs and FEniCS etc. The
> >>> capabilities that such a library would provide would be the same if it
> >>> was part of SymPy or just a separate library. Its capabilities would
> >>> be limited though by the capabilities of the core parts of SymPy. When
> >>> I look at GSOC proposals I am much more interested in proposals that
> >>> improve existing core functionality that would provide a good
> >>> foundation for other things to be built on top.
> >>>
> >>> If SymPy did not have pdsolve and someone proposed it in a GSOC
> >>> project now then I would say that we should reject the proposal. The
> >>> only way that I would be happy to add pdsolve is if the code was
> >>> already written, comprehensive, well tested, with a well defined scope
> >>> and had already been proven to be useful. As it is we have a function
> >>> that is not that useful and would still not be that useful even if
> >>> someone improved it a bit. I'm not interested in having a GSOC project
> >>> that tries to improve pdsolve rather than some other project that
> >>> improves some core part of SymPy.
> >>>
> >>> It is possible that someone else would be interested in supervising a
> >>> project around pdsolve but personally I would not and I would not
> >>> consider it to be any kind of priority for SymPy's roadmap. The
> >>> priorities from my perspective are more things like core algebra,
> >>> polynomials and matrices, functions like solve, limits, series, code
> >>> generation, performance, etc. If these parts of SymPy are good then it
> >>> provides a good foundation for someone to make a symbolic PDE library
> >>> but if we don't have the resources to handle the core things then we
> >>> should not spend those resources on things that could just be in other
> >>> libraries that depend on SymPy.
> >>>
> >>> Oscar
> >
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