Dr. Chen Zhaolin is a Doctor of Engineering and Associate Professor. He received his PhD from Northwestern Polytechnical University in 2020. From 2021 to 2026, he successively worked as a postdoctoral researcher at Northwestern Polytechnical University and as a Research Associate at The Hong Kong Polytechnic University. He joined Nanjing University of Aeronautics and Astronautics in January 2026.

His research focuses on aircraft structural dynamics and aeroelasticity, particularly the aerothermoelastic analysis of hypersonic vehicles and artificial intelligence algorithms for aeroelastic problems.

He has published one academic monograph and more than 20 research papers in journals covering mechanics, aerospace engineering, and artificial intelligence, including Computer Methods in Applied Mechanics and Engineering (CMAME), Chinese Journal of Aeronautics (CJA), Thin-Walled Structures (TWS), and Advanced Engineering Informatics (AEI). Two of his papers have been selected as ESI Highly Cited Papers.

He has led three research projects, including a Young Scientists Fund project of the National Natural Science Foundation of China (NSFC), and participated as a key research member in more than 10 NSFC and aviation industry research projects. He has received three research awards, including the Second Prize for Scientific and Technological Progress from the Chinese Society for Vibration Engineering.

Research Interests 1. Aerothermoelastic Analysis of Hypersonic Vehicles: Developing accurate and efficient multi-physics coupled dynamic analysis methods for nonlinear aerothermoelastic problems involving fluid, structural, and thermal interactions in hypersonic vehicles. Studying the evolution of aerothermoelastic structural behavior and revealing how different physical parameters affect structural responses. 2. Artificial Intelligence Algorithms for Aeroelastic Problems: Applying advanced artificial intelligence technologies, including Physics-Informed Neural Networks (PINNs) and Neural Operators, to develop interpretable neural network algorithms. Building physics-data hybrid models for aeroelastic systems to achieve efficient and accurate solutions to complex aeroelastic problems.

Representative Publications 1. Chen Zhaolin, Yang Zhicheng, Zhang Yiting, et al. (2026). A novel Fourier time-sequential PINN approach for multi-frequency analysis of nonlinear aerothermoelastic problems. Advanced Engineering Informatics, 72: 104446. 2. Chen Zhaolin, Du Yueming, Gu Yingsong, et al. (2025). A stochastic energy finite element method for predicting the high-frequency dynamic response of panels under aero-thermo-acoustic loads. Chinese Journal of Aeronautics, 38: 103613. 3. Chen Zhaolin, Lai Siu-Kai, Yang Zhicheng, et al. (2025). AT-PINN-HC: A refined time-sequential method incorporating hard-constraint strategies for predicting structural behavior under dynamic loads. Computer Methods in Applied Mechanics and Engineering, 436: 117691. (ESI Highly Cited Paper) 4. Chen Zhaolin, Yang Zhichun, Gu Yingsong, et al. (2024). High-frequency vibration analysis of panels under aerothermoelastic effects in supersonic airflow by an energy finite element method. Thin-Walled Structures, 200: 111885. 5. Chen Zhaolin, Lai Siu-Kai, Yang Zhichun. (2024). AT-PINN: Advanced time-marching physics-informed neural network for structural vibration analysis. Thin-Walled Structures, 196: 111423. (ESI Highly Cited Paper) 6. Chen Zhaolin, Yang Zhichun. (2023). Energy Finite Element Method for Predicting High-Frequency Structural Vibration Responses. Northwestern Polytechnical University Press.

Research Awards 1. Second Prize, Military Science and Technology Progress Award, 2025 (Ranked 3rd among 7 recipients). 2. Second Prize, Shaanxi Higher Education Scientific and Technological Research Outstanding Achievement Award, 2024 (Ranked 2nd among 4 recipients). 3. Second Prize, Science and Technology Award of the Chinese Society for Vibration Engineering, 2023 (Ranked 2nd among 5 recipients).

• Aerothermoelastic analysis of hypersonic vehicles and physics-informed AI (PINN, neural operator) algorithms for aeroelastic problems