Discovering unmodeled components in astrodynamics with symbolic regression

Manzi, Matteo and Vasile, Massimiliano; (2020) Discovering unmodeled components in astrodynamics with symbolic regression. In: 2020 IEEE Congress on Evolutionary Computation (CEC). IEEE, GBR. ISBN 9781728169293 (https://doi.org/10.1109/CEC48606.2020.9185534)

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Abstract

The paper explores the use of symbolic regression to discover missing parts of the dynamics of space objects from tracking data. The starting assumption is that the differential equations governing the motion of an observable object are incomplete and do not allow a correct prediction of the future state of that object. Symbolic regression, making use of Genetic Programming (GP), coupled with a sensitivity analysis-based parameter estimation, is proposed to reconstruct the missing parts of the dynamic equations from sparse measurements of position and velocity. Furthermore, the paper explores the effect of uncertainty in tracking measurements on the ability of GP to recover the correct structure of the dynamic equations. The paper presents a simple, yet representative, example of incomplete orbital dynamics to test the use of symbolic regression.

  • Item type:Book Section
    ID code:72508
    Dates:
    Date
    Event
    3 September 2020
    Published
    15 March 2020
    Accepted
    Notes:© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Technology > Motor vehicles. Aeronautics. Astronautics
    Department:Faculty of Engineering > Mechanical and Aerospace Engineering
    Depositing user: Pure Administrator
    Date deposited:01 Jun 2020 08:36
    Last modified:08 Apr 2024 14:01
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/72508
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