Hi Jason,

cheers! The interoperability with SymPy and its mechanics module is a work
in progress, so please let me know what you think we could improve on.

Kind regards,

  Francesco.

On Fri, 1 Oct 2021 at 09:12, Jason Moore <[email protected]> wrote:

> Hi,
>
> Thanks for sharing. I'll try it out with some other mechanics problems.
> Looks nice!
>
> Jason
> moorepants.info
> +01 530-601-9791
>
>
> On Wed, Sep 29, 2021 at 11:23 PM Francesco Biscani <[email protected]>
> wrote:
>
>> Hello,
>>
>> we just released the latest version of our Taylor integrator heyoka.py:
>>
>> https://github.com/bluescarni/heyoka.py
>>
>> heyoka.py is an implementation of Taylor's method for the numerical
>> integration of systems of ODEs based on automatic differentiation and
>> just-in-time compilation via LLVM.
>>
>> Current features include:
>>
>> - support for both double-precision and extended-precision floating-point
>> types,
>> - the ability to maintain machine precision accuracy over tens of
>> billions of timesteps,
>> - high-precision zero-cost dense output,
>> - accurate and reliable event detection,
>> - excellent performance,
>> - batch mode integration to harness the power of modern SIMD instruction
>> sets.
>>
>> heyoka.py needs to represent the ODEs symbolically in order to apply the
>> automatic differentiation rules necessary for an efficient implementation
>> of Taylor's method. For this purpose, heyoka.py uses its own expression
>> system, but in recent versions we added the ability to convert heyoka.py's
>> symbolic expressions to/from SymPy. Here's a simple example of
>> interoperability between heyoka.py and SymPy:
>>
>> https://bluescarni.github.io/heyoka.py/notebooks/sympy_interop.html
>>
>> Here instead is a non-trivial example where the equations of motion are
>> formulated via SymPy's classical mechanics module and then integrated via
>> heyoka.py:
>>
>> https://bluescarni.github.io/heyoka.py/notebooks/tides_spokes.html
>>
>> This second example also shows how the common subexpression elimination
>> capabilities of heyoka.py were able to drastically simplify highly-complex
>> Lagrangian equations.
>>
>> As a long-time observer/user of SymPy, I thought that other SymPy users
>> might find this project interesting. I am also looking for feedback on our
>> SymPy conversions facilities, as this is my first time digging into the
>> SymPy expression system internals.
>>
>> Thanks and kind regards,
>>
>>   Francesco
>>
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