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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.
|Title of host publication||2020 IEEE Congress on Evolutionary Computation (CEC)|
|Place of Publication||Piscataway, NJ|
|Number of pages||7|
|Publication status||Published - 3 Sept 2020|
|Event||IEEE World Congress on Computational Intelligence 2020 - Glasgow, United Kingdom|
Duration: 19 Jul 2020 → 24 Jul 2020
|Conference||IEEE World Congress on Computational Intelligence 2020|
|Period||19/07/20 → 24/07/20|
- symbolic regression
- genetic programming
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- 1 Finished
Stardust-R (Stardust Reloaded) H2020 MCSA ITN 2018
Vasile, M., Feng, J., Fossati, M., Maddock, C., Minisci, E. & Riccardi, A.
European Commission - Horizon 2020
1/01/19 → 31/12/22