Lev Pyatko

ul. Tallinskaya 34, 123458 Moscow, Russia
HSE University

Publications:

Pyatko L., Aksenov S.
A Neural Network Approach for Generating Libration Point Orbits
2026, vol. 31, no. 5, pp.  794–813
Abstract
In the circular restricted three-body problem, families of periodic and quasi-periodic orbits exist in the vicinity of the libration points and are of significant interest for practical applications. This work presents a neural network-based algorithm for orbit construction and station-keeping, applicable to both periodic and quasi-periodic orbits near collinear libration points. The method employs a fully connected neural network trained on labeled trajectory data and iteratively refined using trajectories generated during previous stages of the algorithm. The proposed approach was applied to orbit generation near the Sun-Earth L1 libration point and demonstrated the ability to generalize to a broader class of trajectories than those included in the training set.
Keywords: circular restricted three-body problem (CR3BP), libration point orbits (LPO), quasiperiodic orbits, neural network
Citation: Pyatko L., Aksenov S.,  A Neural Network Approach for Generating Libration Point Orbits, Regular and Chaotic Dynamics, 2026, vol. 31, no. 5, pp. 794–813
DOI:10.1134/S1560354726050072

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