Cheers, let me know if you have any issue/question.

Kind regards,

  Francesco.

On Fri, 1 Oct 2021 at 09:37, Jason Moore <[email protected]> wrote:

> Francesco,
>
> I'm prepping a new course I teach at TU Delft in multibody dynamics and
> one week is about numerical integration. I'll try things out when I'm
> working on those lessons.
>
> Jason
> moorepants.info
> +01 530-601-9791
>
>
> On Fri, Oct 1, 2021 at 9:29 AM Francesco Biscani <[email protected]>
> wrote:
>
>> 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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