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Multi-source Aerodynamic Data Fusion Modeling Based on PINN
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Yifeng YANG1, Lin SHEN2, Enpeng QIU2, Zhen CHEN1, Suozhu WANG1
Missiles and Space Vehicles | 2025, 48(5) : 73 - 81
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Missiles and Space Vehicles | 2025, 48(5): 73-81
• Aerodynamic Parameter Identification and Application Studies •
Multi-source Aerodynamic Data Fusion Modeling Based on PINN
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Yifeng YANG1, Lin SHEN2, Enpeng QIU2, Zhen CHEN1, Suozhu WANG1
Affiliations
  • 1.Beijing Institute of Space Long March Vehicle, Beijing, 100076
  • 2.College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016
Published: 2025-10-25 doi: 10.7654/j.issn.2097-1974.20250510
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Establishing an accurate aerodynamic model is of great significance for analyzing the aerodynamic characteristics of aircraft and designing reliable flight control systems during the aircraft design process. Multi-source data fusion of aerodynamic data from different sources, such as wind tunnel tests and flight tests, is currently a popular method for unsteady aerodynamic modeling by intelligent algorithms. However, traditional fusion algorithms have shortcomings such as high requirements for flight data sources and weak generalization capabilities. Thus an improved intelligent modeling method based on Physics-Informed Neural Networks (PINNs) is proposed to integrate static wind tunnel test data and flight data. Compared to traditional PINNs, the physical loss constraints in improved PINN are reversely constructed to enable feature extraction from different and discrete flight data. The static wind tunnel data are incorporated into both the input and loss function of neural network to construct residual estimates. So the differences between ground and flight aerodynamic data are effectively corrected. The predictive aerodynamic characteristics for different motion forms demonstrate that the improved PINN not only has high aerodynamic prediction accuracy but also exhibits excellent generalization capabilities.

multi-source data fusion  /  Physics-Informed Neural Networks  /  aerodynamic modeling
Yifeng YANG, Lin SHEN, Enpeng QIU, Zhen CHEN, Suozhu WANG. Multi-source Aerodynamic Data Fusion Modeling Based on PINN[J]. Missiles and Space Vehicles, 2025 , 48 (5) : 73 -81 . DOI: 10.7654/j.issn.2097-1974.20250510
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Year 2025 volume 48 Issue 5
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Article Info
doi: 10.7654/j.issn.2097-1974.20250510
  • Receive Date：2025-02-22
  • Online Date：2025-11-27
  • Published：2025-10-25
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  • Received：2025-02-22
  • Revised：2025-05-15
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Affiliations
    1.Beijing Institute of Space Long March Vehicle, Beijing, 100076
    2.College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
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Percentage of
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Genus		 种数
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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