, Volume 3, Issue 4, pp 301–324 | Cite as

Representing dynamics in the eccentric Hill system using a neural network architecture

  • Stijn De SmetEmail author
  • Daniel J. Scheeres
  • Jeffrey S. Parker
Research Article


This paper demonstrates how artificial neural networks can be used to alleviate common problems encountered when creating a large database of Poincaré map responses. A general architecture is developed using a combination of regression and classification feedforward neural networks. This allows one to predict the response of the Poincaré map, as well as to identify anomalies, such as impact or escape. Furthermore, this paper demonstrates how an artificial neural network can be used to predict the error between a more complex and a simpler dynamical system. As an example application, the developed architecture is implemented on the Sun-Mars eccentric Hill system. Error statistics of the entire architecture are computed for both one Poincaré map and for iterated maps. The neural networks are then applied to study the long-term impact and escape stability of trajectories in this system.


periapse Poincaré map artificial neural networks eccentric Hill system 



This research has been performed through funding provided by Advanced Space. This work utilized the RMACC Summit supercomputer, which is supported by the National Science Foundation (awards ACI-1532235 and ACI-1532236), the University of Colorado Boulder, and Colorado State University. The Summit supercomputer is a joint effort of the University of Colorado Boulder and Colorado State University.


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Copyright information

© Tsinghua University Press 2019

Authors and Affiliations

  • Stijn De Smet
    • 1
    Email author
  • Daniel J. Scheeres
    • 1
  • Jeffrey S. Parker
    • 2
  1. 1.Department of Aerospace Engineering SciencesThe University of Colorado BoulderBoulderUSA
  2. 2.Advanced SpaceBoulderUSA

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