Revisiting high-order Taylor methods for astrodynamics and celestial mechanics
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Abstract (arXiv)
We present heyoka, a new, modern and general-purpose implementation of Taylor's integration method for the numerical solution of ordinary differential equations. Detailed numerical tests focused on difficult high-precision gravitational problems in astrodynamics and celestial mechanics show how our general-purpose integrator is competitive with and often superior to state-of-the-art specialised symplectic and non-symplectic integrators in both speed and accuracy. In particular, we show how Taylor methods are capable of satisfying Brouwer's law for the conservation of energy in long-term integrations of planetary systems over billions of dynamical timescales. We also show how close encounters are modelled accurately during simulations of the formation of the Kirkwood gaps and of Apophis' 2029 close encounter with the Earth (where heyoka surpasses the speed and accuracy of domain-specific methods). heyoka can be used from both C++ and Python, and it is publicly available as an open-source project.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 3 May 2021.
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Citing papers per year: 2022: 2; 2023: 1; 2024: 2; 2025: 3; 2026: 1.
9 papers.
- 2022Neural representation of a time optimal, constant acceleration rendezvouscited by 10
- 2024Certifying Guidance & Control Networks: Uncertainty Propagation to an Event Manifoldcited by 1
- 2025High-order expansion of Neural Ordinary Differential Equations flowscited by 1
- 2022The Fellowship of the Dyson Ring: ACT&Friends' Results and Methods for GTOC 11cited by 0
- 2023Deep Learning of Dynamical System Parameters from Return Maps as Imagescited by 0
- 2024NeuralODEs for VLEO simulations: Introducing thermoNET for Thermosphere Modelingcited by 0
- 2025EclipseNETs: Learning Irregular Small Celestial Body Silhouettescited by 0
- 2025Comparing Behavioural Cloning and Reinforcement Learning for Spacecraft Guidance and Control Networkscited by 0
- 2026Continuous Learning of Gravity Field Irregularities Around Small Bodies via Neural Hamiltonian ODEscited by 0
- 2026Continuous Learning of Gravity Field Irregularities Around Small Bodies via Neural Hamiltonian ODEs10 Sep 2026
- 2025Comparing Behavioural Cloning and Reinforcement Learning for Spacecraft Guidance and Control Networks22 Jul 2025
- 2025EclipseNETs: Learning Irregular Small Celestial Body Silhouettes6 Apr 2025
- 2025High-order expansion of Neural Ordinary Differential Equations flows2 Apr 2025
- 2024Certifying Guidance & Control Networks: Uncertainty Propagation to an Event Manifold30 Sep 2024
- 2024NeuralODEs for VLEO simulations: Introducing thermoNET for Thermosphere Modeling29 May 2024
- 2023Deep Learning of Dynamical System Parameters from Return Maps as Images20 Jun 2023
- 2022The Fellowship of the Dyson Ring: ACT&Friends' Results and Methods for GTOC 1113 May 2022
- 2022Neural representation of a time optimal, constant acceleration rendezvous29 Mar 2022