heyoka 3.1.0
This new release of heyoka 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 of the Python bindings 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.
Fixes
- Improve the behaviour of the in-memory cache by ensuring that global constants are always defined in a deterministic order in an LLVM module.
- Improve the numerical stability of the Kepler solvers.
- Fix compiler warning when building without SLEEF support.
As usual, the full changelog is available here: