Kolmogorov–Arnold Networks for Reduced-Order Modeling in Unsteady Aerodynamics and Aeroelasticity
Kolmogorov–Arnold Networks (KANs) are a recent development in machine learning, offering strong functional representation capabilities, enhanced interpretability, and reduced parameter complexity. Leveraging these advantages, this paper proposes a KAN-based reduced-order model (ROM) for unsteady aer...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/11/5820 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850129856556171264 |
|---|---|
| author | Yuchen Zhang Han Tang Lianyi Wei Guannan Zheng Guowei Yang |
| author_facet | Yuchen Zhang Han Tang Lianyi Wei Guannan Zheng Guowei Yang |
| author_sort | Yuchen Zhang |
| collection | DOAJ |
| description | Kolmogorov–Arnold Networks (KANs) are a recent development in machine learning, offering strong functional representation capabilities, enhanced interpretability, and reduced parameter complexity. Leveraging these advantages, this paper proposes a KAN-based reduced-order model (ROM) for unsteady aerodynamics and aeroelasticity. To effectively capture temporal dependencies inherent in nonlinear unsteady flow phenomena, an architecture termed Kolmogorov–Arnold Gated Recurrent Network (KAGRN) is introduced. By incorporating a recurrent structure and a gating mechanism, the proposed model effectively captures time-delay effects and enables the selective control and preservation of long-term temporal dependencies. This architecture provides high predictive accuracy, good generalization capability, and fast prediction speed. The performance of the model is evaluated using simulations of the NACA (National Advisory Committee for Aeronautics) 64A010 airfoil undergoing harmonic motion and limit cycle oscillations in transonic flow conditions. Results demonstrate that the proposed model can not only accurately and efficiently predict unsteady aerodynamic coefficients, but also effectively capture nonlinear aeroelastic responses. |
| format | Article |
| id | doaj-art-7abf0bd68188429ab7091459a67adfc8 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-7abf0bd68188429ab7091459a67adfc82025-08-20T02:32:50ZengMDPI AGApplied Sciences2076-34172025-05-011511582010.3390/app15115820Kolmogorov–Arnold Networks for Reduced-Order Modeling in Unsteady Aerodynamics and AeroelasticityYuchen Zhang0Han Tang1Lianyi Wei2Guannan Zheng3Guowei Yang4Institute of Mechanics, Chinese Academy of Sciences, Beijing 100190, ChinaInstitute of Mechanics, Chinese Academy of Sciences, Beijing 100190, ChinaInstitute of Mechanics, Chinese Academy of Sciences, Beijing 100190, ChinaInstitute of Mechanics, Chinese Academy of Sciences, Beijing 100190, ChinaInstitute of Mechanics, Chinese Academy of Sciences, Beijing 100190, ChinaKolmogorov–Arnold Networks (KANs) are a recent development in machine learning, offering strong functional representation capabilities, enhanced interpretability, and reduced parameter complexity. Leveraging these advantages, this paper proposes a KAN-based reduced-order model (ROM) for unsteady aerodynamics and aeroelasticity. To effectively capture temporal dependencies inherent in nonlinear unsteady flow phenomena, an architecture termed Kolmogorov–Arnold Gated Recurrent Network (KAGRN) is introduced. By incorporating a recurrent structure and a gating mechanism, the proposed model effectively captures time-delay effects and enables the selective control and preservation of long-term temporal dependencies. This architecture provides high predictive accuracy, good generalization capability, and fast prediction speed. The performance of the model is evaluated using simulations of the NACA (National Advisory Committee for Aeronautics) 64A010 airfoil undergoing harmonic motion and limit cycle oscillations in transonic flow conditions. Results demonstrate that the proposed model can not only accurately and efficiently predict unsteady aerodynamic coefficients, but also effectively capture nonlinear aeroelastic responses.https://www.mdpi.com/2076-3417/15/11/5820Kolmogorov–Arnold Networksreduced-order modelunsteady aerodynamicsnonlinear aeroelasticity |
| spellingShingle | Yuchen Zhang Han Tang Lianyi Wei Guannan Zheng Guowei Yang Kolmogorov–Arnold Networks for Reduced-Order Modeling in Unsteady Aerodynamics and Aeroelasticity Applied Sciences Kolmogorov–Arnold Networks reduced-order model unsteady aerodynamics nonlinear aeroelasticity |
| title | Kolmogorov–Arnold Networks for Reduced-Order Modeling in Unsteady Aerodynamics and Aeroelasticity |
| title_full | Kolmogorov–Arnold Networks for Reduced-Order Modeling in Unsteady Aerodynamics and Aeroelasticity |
| title_fullStr | Kolmogorov–Arnold Networks for Reduced-Order Modeling in Unsteady Aerodynamics and Aeroelasticity |
| title_full_unstemmed | Kolmogorov–Arnold Networks for Reduced-Order Modeling in Unsteady Aerodynamics and Aeroelasticity |
| title_short | Kolmogorov–Arnold Networks for Reduced-Order Modeling in Unsteady Aerodynamics and Aeroelasticity |
| title_sort | kolmogorov arnold networks for reduced order modeling in unsteady aerodynamics and aeroelasticity |
| topic | Kolmogorov–Arnold Networks reduced-order model unsteady aerodynamics nonlinear aeroelasticity |
| url | https://www.mdpi.com/2076-3417/15/11/5820 |
| work_keys_str_mv | AT yuchenzhang kolmogorovarnoldnetworksforreducedordermodelinginunsteadyaerodynamicsandaeroelasticity AT hantang kolmogorovarnoldnetworksforreducedordermodelinginunsteadyaerodynamicsandaeroelasticity AT lianyiwei kolmogorovarnoldnetworksforreducedordermodelinginunsteadyaerodynamicsandaeroelasticity AT guannanzheng kolmogorovarnoldnetworksforreducedordermodelinginunsteadyaerodynamicsandaeroelasticity AT guoweiyang kolmogorovarnoldnetworksforreducedordermodelinginunsteadyaerodynamicsandaeroelasticity |