Discovering unmodeled components in astrodynamics with symbolic regression

Matteo Manzi; Massimiliano Vasile

doi:10.1109/CEC48606.2020.9185534

Discovering unmodeled components in astrodynamics with symbolic regression

Matteo Manzi, Massimiliano Vasile

Mechanical And Aerospace Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

5 Citations (Scopus)

87 Downloads (Pure)

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.

Original language	English
Title of host publication	2020 IEEE Congress on Evolutionary Computation (CEC)
Place of Publication	Piscataway, NJ
Publisher	IEEE
Number of pages	7
ISBN (Electronic)	9781728169293
ISBN (Print)	9781728169309
DOIs	https://doi.org/10.1109/CEC48606.2020.9185534
Publication status	Published - 3 Sept 2020
Event	IEEE World Congress on Computational Intelligence 2020 - Glasgow, United Kingdom Duration: 19 Jul 2020 → 24 Jul 2020 https://wcci2020.org/

Conference

Conference	IEEE World Congress on Computational Intelligence 2020
Abbreviated title	WCCI
Country/Territory	United Kingdom
City	Glasgow
Period	19/07/20 → 24/07/20
Internet address	https://wcci2020.org/

Keywords

symbolic regression
genetic programming
astrodynamics
astronautics

Access to Document

10.1109/CEC48606.2020.9185534

Manzi-Vasile-IEEE-WCCI-2020-Discovering-unmodeled-components-in-astrodynamicsAccepted author manuscript, 251 KB

Cite this

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title = "Discovering unmodeled components in astrodynamics with symbolic regression",

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.",

keywords = "symbolic regression, genetic programming, astrodynamics, astronautics",

author = "Matteo Manzi and Massimiliano Vasile",

note = "{\textcopyright} 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.; IEEE World Congress on Computational Intelligence 2020, WCCI ; Conference date: 19-07-2020 Through 24-07-2020",

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AU - Manzi, Matteo

AU - Vasile, Massimiliano

N1 - © 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.

PY - 2020/9/3

Y1 - 2020/9/3

N2 - 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.

AB - 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.

KW - symbolic regression

KW - genetic programming

KW - astrodynamics

KW - astronautics

UR - https://wcci2020.org/

U2 - 10.1109/CEC48606.2020.9185534

DO - 10.1109/CEC48606.2020.9185534

M3 - Conference contribution book

SN - 9781728169309

BT - 2020 IEEE Congress on Evolutionary Computation (CEC)

PB - IEEE

CY - Piscataway, NJ

T2 - IEEE World Congress on Computational Intelligence 2020

Y2 - 19 July 2020 through 24 July 2020

ER -

Discovering unmodeled components in astrodynamics with symbolic regression

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Stardust-R (Stardust Reloaded) H2020 MCSA ITN 2018

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Discovering unmodeled components in astrodynamics with symbolic regression

Abstract

Conference

Keywords

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Stardust-R (Stardust Reloaded) H2020 MCSA ITN 2018

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