arXiv:2606.00895v1 Announce Type: cross Abstract: This paper presents a fast, recursive neural solver for the J2-perturbed Lambert problem based on Tiny Recursive Models (TRM), termed the TRM-Perturbed Lambert (TRM-PL) model. TRM is a weight-shared architecture whose effective capacity emerges from iteration depth rather than parameter count: a compact reasoning module is applied repeatedly within a two-level latent hierarchy, refining a candidate departure velocity by simulating the J2 trajectory and correcting it from the resulting tracking error. This unifies initial-guess generation and it
The continuous advancements in AI, particularly in efficient and specialized neural network architectures, are enabling more complex scientific and engineering problems to be tackled with novel computational approaches.
This development offers a potentially faster and more energy-efficient method for critical space trajectory calculations, impacting satellite operations, space defence, and future space exploration.
Traditional computational methods for orbital mechanics, which are often computationally intensive, might be supplemented or replaced by compact, iterative AI models, improving real-time decision-making in space applications.
- · Space agencies
- · Satellite operators
- · Aerospace defence contractors
- · AI hardware developers
- · Legacy orbital mechanics software providers
- · High-latency space communication systems
Faster and more accurate trajectory planning for satellites and spacecraft, especially in perturbed orbital environments.
Reduced computational power requirements for on-board autonomous systems in space, enabling more complex tasks with fewer resources.
Enhanced defensive and offensive capabilities in space, as AI-powered systems can react and maneuver more quickly and unpredictably.
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Read at arXiv cs.LG