Comparison of Selected Machine Learning Tools for Identification of Jet Engine Dynamics
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60162694%3AG43__%2F26%3A00564328" target="_blank" >RIV/60162694:G43__/26:00564328 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/11061307" target="_blank" >https://ieeexplore.ieee.org/document/11061307</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICMT65201.2025.11061307" target="_blank" >10.1109/ICMT65201.2025.11061307</a>
Alternative languages
Result language
angličtina
Original language name
Comparison of Selected Machine Learning Tools for Identification of Jet Engine Dynamics
Original language description
This paper addresses an application of machine learning to identify dynamical model of a gas turbine engine. Fundamental aspects of component modeling and calculation of both the steady state and the transient operation are briefly described. Subsequently, models of spool speed dynamics were identified via the neural network, the sparse identification of nonlinear dynamics, and the Koopman operator theory. Concerning the second mentioned approach, the utilization of nonlinear parameters of basis functions for sparsity promotion is proposed. The results show similar performance of considered approaches, while their complexity and interpretability differ severely. Furthermore, control-oriented advantages of sparse identification of dynamics and Koopman model are discussed.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20304 - Aerospace engineering
Result continuities
Project
—
Continuities
S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2025 10th International Conference on Military Technologies, ICMT 2025 - Proceedings
ISBN
979-8-3315-2338-1
ISSN
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e-ISSN
2996-4474
Number of pages
7
Pages from-to
1-7
Publisher name
Institute of Electrical and Electronics Engineers Inc.
Place of publication
Brno
Event location
Brno, Czech Republic
Event date
May 27, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001545807300049