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 <bluesca...@gmail.com>
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 <moorepa...@gmail.com> 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 <bluesca...@gmail.com>
>> 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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