研究领域

强化学习算法研究（基础理论与算法创新）： 强化学习算法的收敛性，泛化性，迁移性研究，探索算法，0 阶优化算法等算法的创新和效率研究，基于模型的强化学习算法；2. 强化学习驱动的大模型推理性能研究（推理能力的刻画，多模态推理）：推理能力的刻画与优化，多模态大模型的推理能力研究，在自主科研智能体方面的应用；3. 强化学习在科学智能中的应用（流体力学，分子科学）。

代表性学术论文

[1]    Haodong Feng, Yue Wang, Dixia Fan. “How to Re-enable PDE Loss for Physical Systems Modeling Under Partial Observation” AAAI 2025.

[2]    “Monte Carlo Neural PDE Solver for Learning PDEs via Probabilistic Representation” TPAMI 2025.

[3]    Nian Ran, Peng Xiao, Yue Wang, Wesley Shi, Jianxin Lin, Qi Meng, Richard Allmendinger. “HR-Extreme: A High-Resolution Dataset for Extreme Weather Forecasting” ICLR 2025.

[4]    Peiyan Hu, Yue Wang, and Zhi-Ming Ma. "Better neural PDE solvers through data-free mesh movers." ICLR 2024.

[5]    Haodong Feng, Yue Wang, Hui Xiang, Zhiyang Jin, and Dixia Fan. "How to Control Hydrodynamic Force on Fluidic Pinball via Deep Reinforcement Learning." Physics of Fluids. 35, no. 4. 2023.

[6]    Jinhua Zhu, Yue Wang, Lijun Wu, Tao Qin, Wengang Zhou, Tie-Yan Liu, and Houqiang Li. "Making Better Decision by Directly Planning in Continuous Control." ICLR 2023.

[7]    Xinquan Huang, Wenlei Shi, Qi Meng, Yue Wang, Xiaotian Gao, Jia Zhang, and Tie-Yan Liu. "NeuralStagger: Accelerating Physics-constrained Neural PDE Solver with Spatial-temporal Decomposition." ICML 2023.

[8]    Chongchong Li, Yue Wang, Wei Chen, Yuting Liu, Zhi-Ming Ma, and Tie-Yan Liu. "Gradient information matters in policy optimization by back-propagating through model." ICLR. 2022.

[9]    Rui Zhang, Peiyan Hu, Qi Meng, Yue Wang, Rongchan Zhu, Bingguang Chen, Zhi-Ming Ma, and Tie-Yan Liu. "DRVN (deep random vortex network): A New Physics-Informed Machine Learning Method for Simulating and Inferring Incompressible Fluid Flows." Physics of Fluids. 34, no. 10. 2022.

[10]    Yue Wang, Yuting Liu, Wei Chen, Zhi-Ming Ma, and Tie-Yan Liu. "Target transfer Q-learning and its convergence analysis." Neurocomputing 392 (2020): 11-22.

[11]    Yue Wang, Qi Meng, Wei Chen, Yuting Liu, Zhi-Ming Ma and Tie-Yan Liu. “Target Transfer Q-Learning and Its Convergence Analysis.” Neurocomputing 2020.

[12]    Yue Wang, Wei Chen, Yuting Liu, Zhi-Ming Ma, and Tie-Yan Liu. "Finite sample analysis of the GTD policy evaluation algorithms in Markov setting." NeurIPS 2017.

[13]    Qi Meng, Yue Wang, Wei Chen, Taifeng Wang, Zhi-Ming Ma and Tie-Yan Liu. “Generalization Error Bounds for Optimization Algorithms via Stability.” AAAI 2017.