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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