Learning capabilities of neural networks and Keplerian dynamics

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

1 Scopus citations

Abstract

Machine learning (ML) tools, especially deep neural networks (DNNs) have garnered significant attention in the last decade; however, it is not clear whether ML tools can learn the inherent characteristics of dynamical model (such as conservation laws) from the training data set. This paper considers the effectiveness of DNNs in learning dynamical system models by considering the Keplerian two-body problem. Training a DNN with data from a single revolution produces poor performance when predicting motion on subsequent revolutions. By incorporating deviations from constancy of angular momentum and total energy into the loss function for the DNN, predictive performance improves significantly. Further improvements appear when a richer training data set (generated from a number of orbits with different in orbital element values) is employed.

Original languageEnglish (US)
Title of host publicationAAS/AIAA Astrodynamics Specialist Conference, 2018
EditorsPuneet Singla, Ryan M. Weisman, Belinda G. Marchand, Brandon A. Jones
PublisherUnivelt Inc.
Pages2293-2310
Number of pages18
ISBN (Print)9780877036579
StatePublished - Jan 1 2018
EventAAS/AIAA Astrodynamics Specialist Conference, 2018 - Snowbird, United States
Duration: Aug 19 2018Aug 23 2018

Publication series

NameAdvances in the Astronautical Sciences
Volume167
ISSN (Print)0065-3438

Conference

ConferenceAAS/AIAA Astrodynamics Specialist Conference, 2018
CountryUnited States
CitySnowbird
Period8/19/188/23/18

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering
  • Space and Planetary Science

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  • Cite this

    Guého, D., Singla, P., & Melton, R. G. (2018). Learning capabilities of neural networks and Keplerian dynamics. In P. Singla, R. M. Weisman, B. G. Marchand, & B. A. Jones (Eds.), AAS/AIAA Astrodynamics Specialist Conference, 2018 (pp. 2293-2310). (Advances in the Astronautical Sciences; Vol. 167). Univelt Inc..