Rapid approximation of invariant manifolds using machine learning methods

Vishwa Shah, Ryne Beeson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


Low-energy mission design in the three-body model leverages invariant manifolds to obtain low-propellant solutions. Optimizing these trajectories requires generating manifolds and searching for the optimal manifold insertion point. Typically, manifolds are generated using numerical methods which can take up to several seconds, thus making the generation of these structures in an optimization framework computationally intractable. In this paper we will explore the application of machine learning algorithms to enable rapid approximation of these structures. The regression models will then be used within an optimization framework. The robustness, accuracy and computational advantages will be benchmarked against Cubic Convolution based approximation methods.

Original languageEnglish (US)
Title of host publicationASTRODYNAMICS 2017
EditorsJohn H. Seago, Nathan J. Strange, Daniel J. Scheeres, Jeffrey S. Parker
PublisherUnivelt Inc.
Number of pages17
ISBN (Print)9780877036456
StatePublished - 2018
Externally publishedYes
EventAAS/AIAA Astrodynamics Specialist Conference, 2017 - Stevenson, United States
Duration: Aug 20 2017Aug 24 2017

Publication series

NameAdvances in the Astronautical Sciences
ISSN (Print)0065-3438


ConferenceAAS/AIAA Astrodynamics Specialist Conference, 2017
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering
  • Space and Planetary Science


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