Discovering unmodeled components in astrodynamics with symbolic regression

Matteo Manzi, Massimiliano Vasile

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

3 Citations (Scopus)
46 Downloads (Pure)


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.
Original languageEnglish
Title of host publication2020 IEEE Congress on Evolutionary Computation (CEC)
Place of PublicationPiscataway, NJ
Number of pages7
ISBN (Electronic)9781728169293
ISBN (Print)9781728169309
Publication statusPublished - 3 Sept 2020
EventIEEE World Congress on Computational Intelligence 2020 - Glasgow, United Kingdom
Duration: 19 Jul 202024 Jul 2020


ConferenceIEEE World Congress on Computational Intelligence 2020
Abbreviated titleWCCI
Country/TerritoryUnited Kingdom
Internet address


  • symbolic regression
  • genetic programming
  • astrodynamics
  • astronautics


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