heyoka.py 3.1.0

[bluescarni]

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

Neural networks

It is now possible to create feed-forward neural networks in the expression system, and use them in the definition of ODEs. See the machine learning section in the documentation for more information and examples.

Complementary to the introduction of neural networks is the addition of the (leaky) ReLU and its derivative to the expression system.

New anomalies

The eccentric longitude F and the delta eccentric anomaly DE have been added to the expression system. These transcendental functions are used in celestial mechanics and astrodynamics to implement Lagrangian propagation and in the definition of equinoctial orbital elements.

Thanks to the introduction of the eccentric longitude F, the analytical ephemeris VSOP2013 should now be more numerically stable for orbits with low eccentricity/inclination.

Performance improvements

Several operations involving the manipulation and differentiation of large symbolic expressions are now substantially faster, often by more than an order of magnitude.

Other

• New example notebook on the continuation of periodic orbits in the CR3BP.
• New example notebook implementing Lagrange propagation.

Fixes

• Improve the behaviour of the in-memory cache.
• Improve the numerical stability of the Kepler solvers.
• Fix slow performance when creating very large compiled functions.
• Fix building against Python 3.12

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 3.1.0.