About Me
Jianhao Fang is currently a Postdoctoral Researcher under the InnoCORE Center Postdoctoral Fellowship, Granted by Ministry of Science and ICT at the Korea Advanced Institute of Science and Technology (KAIST).
He obtained his Ph.D. from the School of Mechanical Engineering, Zhejiang University, under the supervision of Prof. Zhenyu Liu and Prof. Weifei Hu. He completed his undergraduate education at the School of Mechanical Engineering, Dalian University of Technology.
His primary research interests include digital twin technology, artificial intelligence, model order reduction, and uncertainty-based design optimization, with applications in advanced mechanical design and renewable energy systems.
Work Experience
Postdoctoral Researcher
04/2026 – Current
Korea Advanced Institute of Science and Technology (KAIST)
InnoCORE Center Postdoctoral Fellowship, Granted by the Ministry of Science and ICT, South Korea
Education
Ph.D. in Mechanical Engineering
09/2020 – 03/2026
Zhejiang University (ZJU) · Hangzhou, China
Dissertation: "Digital Twins-Driven Spatiotemporal Dependent Reliability-Based Design Optimization for Wind Turbines"
Supervisors: Prof. Zhenyu Liu · Prof. Weifei Hu
Supervisors: Prof. Zhenyu Liu · Prof. Weifei Hu
B.E. in Mechanical Engineering
09/2016 – 06/2020
Dalian University of Technology (DUT) · Dalian, China
Comprehensive Ranking: 1st / 30 · Average Score: 91.51/100
Thesis: "Research on Remote Mirror-Mapping Technology for Virtual and Real States of Robotic Digital Twin"
Thesis: "Research on Remote Mirror-Mapping Technology for Virtual and Real States of Robotic Digital Twin"
Research Interests
🤖 AI for Design
🎯 Reliability-Based Design Optimization
🔄 Digital Twin Technology
💻 Simulation-Based Design
⚙️ Multidisciplinary Design Optimization
🌬️ Wind Energy Systems
Research Area
AI for Engineering Design
Deep reinforcement learning, physics-informed neural networks, and surrogate models applied to engineering design optimization, with comprehensive coverage of AI in wind turbine applications.
Reliability-Based Design Optimization
Novel RBDO methods for spatiotemporal-dependent systems, multi-target reliability frameworks, and adaptive sampling strategies for high-end equipment under service uncertainty.
Digital Twin Technology
Quantitative maturity models, real-time virtual-real state mirror-mapping, and digital twin systems for complex product assembly lines and underground engineering equipment.
Reduced-Order Surrogate Modeling
Reduced-order finite element-informed surrogate models for approximating high-fidelity simulations, enabling efficient spatiotemporal reliability analysis for wind turbine structural components.
Wind Energy Systems
Wind turbine rotor speed optimization, rain erosion analysis, drone-aided surface damage detection, and dynamic wake flow estimation for wind farm applications.
Multidisciplinary Design Optimization
Integration of simulation, AI, and optimization across multiple disciplines, applied to high-end equipment design considering coupled structural, aerodynamic, and reliability constraints.
Publication
14 journal · 9 conference · 1 book chapterB1
Hu W., Fang J., Liu Z., & Tan J.
Wind Energy Engineering (pp. 315–325). Academic Press.
J1
Fang, J., Hu, W., Liao, J., Chen, X., Mo, H., Jin, C., Luo, Y., Liu, Z.
Renewable and Sustainable Energy Reviews, 2025.
J2
Yan, J., Hu, W.*, Zhang, T., Cheng, S., Zhao, F., Fang, J., Wang, D.
ASME Journal of Mechanical Design, 2026. (Accepted)
J3
Zhang, T., Hu, W., Yan, J., Zhao, F., Fang, J., Tang, N., Wu, T.
ASME Journal of Mechanical Design, 2025, 1-42.
J4
Fang, J., Hu, W., Liu, Z., Chen, W., Tan, J., Jiang, Z., Verma, A.S.
Renewable and Sustainable Energy Reviews, 2022, 168, 112788.
J5
Fang, J., Hu, W.*, Liu, Z., Zhou, Y., Wei, C., Tan, J.
Structural and Multidisciplinary Optimization, 2024, 67, 211.
J6
Hu, W., Fang, J., Zhang, T., Liu, Z.*, Tan, J.
Journal of Manufacturing Systems, 2023, 66, 248–259.
J7
Hu, W., Fang, J., Zhang, Y., Liu, Z.*, Verma, A.S., Liu, H., Cong, F., Tan, J.
Renewable Energy, 2024, 122332.
J8
Hu, W., Liao, J., Yan, J., Fang, J., Zhao, F., Lee, I. et al.
Reliability Engineering & System Safety, 2025, 111560.
J9
Wu, X., Lu, W., Wang, K.*, Hu, W., Fang, J., Zha, R.
China Ocean Engineering, 2023, 37, 1011–1021.
R1
A Novel Spatiotemporal-Dependent Reliability Analysis Method Based on a Reduced-Order Surrogate Model for Wind Turbines
Fang J., Hu W., Zhang T. et al.
Submitted to: ASME Journal of Mechanical Design
R2
Dynamic Wind Turbine Wake Estimation Based on Non-Intrusive Reduced Order Modeling
Hu W., Chen X., Fang J.* et al.
Submitted to: Renewable Energy
C1
A Novel Spatiotemporal-Dependent Reliability Analysis Based on A Physics-Informed Reduced-Order Model
Hu W., Fang J., Liao J., Yan J., Zhang T., Zhao F.
ICCES2025, Changsha, China, May 25–29, 2025.
C2
Spatiotemporal-Dependent Structural Reliability Analysis Using a Reduced Order Finite Element-Informed Surrogate Model
Fang J., Hu W., Yan J., Zhang T., Dong N., Wang L., Liu Z., Tan J.
IET Conference Proceedings CP949, Vol. 2025, No. 35. Stevenage, UK.
C3
Spatiotemporal-Dependent Structural Reliability Analysis Based on Reduced Order-Informed Surrogate Model (WCSMO-16)
Hu W.*, Fang J., Yan J.
16th World Congress on Structural and Multidisciplinary Optimization, Kobe, Japan, May 18–23, 2025. (Oral Presentation)
C4
A Deep Reinforcement Learning-Based Optimization Method for Designing Wind Turbine Rotor Speed Considering Rain Erosion
Fang J., Hu W., Liu Z., Chen W., Tan J.
ACSMO 2022, Matsue, Japan, May 22–26, 2022.
C5
Deep Reinforcement Learning Enhanced Convolutional Neural Networks for Robotic Grasping
Fang J., Hu W.*, Wang C., Liu Z., Tan J.
ASME IDETC/CIE2021, Virtual Conference, August 17–20, 2021.
News
Apr 2026
Position
Joined KAIST (Korea Advanced Institute of Science and Technology) as a Postdoctoral Researcher under the InnoCORE Center Fellowship Granted by Ministry of Science and ICT.
Jan 2026
Paper Accepted
Paper "Multi-Target Reliability-Based Design Optimization" accepted by ASME Journal of Mechanical Design (JCR Q1).
2025
Award
Received the China Electricity Council Power Innovation Award (First Class) — one of the highest honors in China's power industry.
2025
Award
Named Excellent Postgraduate Student of Zhejiang University for 2025.
August 2025
Conference
Oral presentation at ASME IDETC-CIE, Anaheim, The Unite State — spatiotemporal reliability analysis for wind turbines.
2025
Paper Published
Review paper on AI-Based Design Optimization for Wind Turbines published in Renewable and Sustainable Energy Reviews (IF: 16.799, Q1).
2024
Paper Published
Two Q1 papers published: Reduced-Order Surrogate Model in SMO and Digital Twin for Wind Turbine Damage Detection in Renewable Energy.
2023
Award
Won Silver Award at the 48th Geneva International Invention Exhibition.
2023
Publication
Book chapter "Intelligent Design and Optimization of Wind Turbines" published in Academic Press Wind Energy Engineering.
Contact
✉️
📞
Phone
Can be obtained by Email
🏛️
Institution
Department of Mechanical Engineering
KAIST, Daejeon, South Korea
KAIST, Daejeon, South Korea
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Academic Profiles
Photography
Beyond research, I enjoy photography in my spare time, focusing on landscape, cityscape, and moments of light and shadow.
