1.  第十届中国科协青年人才托举工程，“折纸结构力学模型辨识及其机械逻辑计算”，项目负责人，2025-2027

2.  国家自然科学基金青年科学基金项目，“面向机械计算的多稳态折纸超材料数据驱动动力学建模与分析”，项目负责人， 2023-2025

3.  中国博士后科学基金第3批特别资助（站前）项目，“基于折展张拉一体化超材料的机器人设计与数据驱动建模”，项目负责人，2019-2023

4. 中国博士后科学基金第70批面上资助，“折展张拉机器人数据驱动动力学建模与性能优化”，项目负责人，2019-2023

5. 上海市超级博士后激励计划，“基于折展张拉一体化超材料的机器人设计与数据驱动建模”，项目负责人，2021-2023

6.  中国力学学会青年人才蓄水池项目，“折纸结构力学模型辨识及其机械逻辑计算”，项目负责人，2023-2024

[1]     Cheng X, Liu Z*, Fang H*. Deep fusion of discrete-time andcontinuous-time models for long-term prediction of chaotic dynamical systems.Nonlinear Dynamics, 113(11): 12545-12563. (2025).

[2]     Liu, Z., Zhang, X., Wang, K. W., Xu, J., & Fang, H.Data-driven modeling of multi-stable origami structures: Extracting the globalgoverning equation and exploring the complex dynamics. Mechanical Systems andSignal Processing, 220, 111659 (2024).

[3]     Liu, Z., He, Z., Hu, X., Sun, Z., Ge, Q., Xu, J., &Fang, H. Origami-Enhanced Mechanical Properties for Worm-Like Robot. Soft Robotics, 12(1), 34-44 (2024). 

[4]     Liu, Z., Fang, H., Xu,J. & Wang, K. W. Cellular Automata Inspired Multistable OrigamiMetamaterials for Mechanical Learning. Advanced Science, 10, 2305146. (2023).

[5]     Liu, Z., Fang, H. & Xu, J. Aneuro-fuzzy network modeling method for uncovering the dynamic properties oftime-varying systems. Mechanical Systems and Signal Processing, 191, 110176.(2023). 

[6]     Liu, Z., Fang, H., Xu, J. & Wang, K.W. Digitized design and mechanical property reprogrammability of multistableorigami metamaterials. Journal of the Mechanics and Physics of Solids, 173, 105237.(2023). 

[7]     Liu, Z., Fang, H., Xu, J. & Wang,K.-W. Discriminative Transition Sequences of Origami Metamaterials forMechanologic. Advanced Intelligent Systems, 5, 2200146. (2023). 

[8]     Liu, Z., Xu, J. & Fang, H. ExtractingInherent Model Structures and Identifying Parameters of Time-Varying SystemsUsing Local Linear Neuro-Fuzzy Networks. IEEE Transactions on Fuzzy Systems,30, 233–247. (2022). 

[9]      Liu, Z., Fang, H. & Xu, J. Identification of piecewiselinear dynamical systems using physically-interpretable neural-fuzzy networks:Methods and applications to origami structures. Neural Networks, 116, 74–87.(2019). 

[10]     Liu, Z., Fang, H., Wang, K.-W. & Xu, J. A parameteridentification method for continuous-time nonlinear systems and its realizationon a Miura-origami structure. Mechanical Systems and Signal Processing, 108,369–386. (2018).