heyoka.py 3.2.0

[bluescarni]

This new release of heyoka.py comes packed with several new features and enhancements.

New AI/ML examples

Thanks to great work by @Sceki and @darioizzo, two new examples have been added to the documentation. The first one explains how to interface pytorch and heyoka.py:

https://bluescarni.github.io/heyoka.py/notebooks/torch_and_heyoka.html

The second one presents an innovative, differentiable model for the Earth's atmospheric density implemented via a neural network:

https://bluescarni.github.io/heyoka.py/notebooks/differentiable_atmosphere.html

Support for single-precision computations

In addition to extended and arbitrary precision computations, heyoka.py now supports also single-precision computations via the NumPy float32 type. Single-precision computations can lead to substantial performance benefits, especially in batch mode and/or low-accuracy applications. See the single-precision tutorial for a usage example.

ELP2000 model

heyoka.py now includes an implementation of the ELP2000 lunar theory. It is thus now possible to formulate systems of differential equations with the time-dependent geocentric lunar position appearing in the right-hand side. See the tutorial for an introduction.

Low-precision vector math

When the fast_math option is active, heyoka.py now employs lower-precision vector implementations of elementary functions, which can lead to substantial speedups in low-accuracy applications. The speedup is particularly visible when using single precision in AI and ML applications.

Miscellanea

• manylinux wheels for Python 3.12 have been added
• Fix wrong truncation to double precision in the dtime setter for the scalar integrator

As usual, the full changelog is available here:

https://bluescarni.github.io/heyoka.py/changelog.html

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This discussion was created from the release heyoka.py 3.2.0.