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publications
Temperature state prediction for lithium-ion batteries based on improved physics informed neural networks
Published in Journal of Energy Storage (IF=8.9), 2023
Yuchen Wang, Can Xiong, Yiming Wang, Po Xu, Changjiang Ju, Jianghao Shi, Genke Yang, and Jian Chu.
Recommended citation: Wang, Y., Xiong, C., Wang, Y., Xu, P., Ju, C., Shi, J., ... & Chu, J. (2023). Temperature state prediction for lithium-ion batteries based on improved physics informed neural networks. Journal of Energy Storage, 73, 108863. https://www.sciencedirect.com/science/article/abs/pii/S2352152X23022600
Accelerating Neural Differential Equations for Irregularly-Sampled Dynamical Systems Using Variational Formulation
Published in ICLR 2024 Workshop on AI4DifferentialEquations In Science, 2024
Hongjue Zhao, Yuchen Wang, Hairong Qi, Jiajia Li, Lui Sha, Han Zhao, and Huajie Shao.
Recommended citation: Zhao, H., Wang, Y., Qi, H., Li, J., Sha, L., Zhao, H., & Shao, H. (2024, March). Accelerating Neural Differential Equations for Irregularly-Sampled Dynamical Systems Using Variational Formulation. In ICLR 2024 Workshop on AI4DifferentialEquations In Science.. https://openreview.net/forum?id=C8tlOCzqll¬eId=l7nUWhiwog
A Deep Transfer Operator Learning Method for Temperature Field Reconstruction in a Lithium-Ion Battery Pack
Published in IEEE Transactions on Industrial Informatics (IF=11.7), 2024
Yuchen Wang, Can Xiong, Changjiang Ju, Genke Yang, Yu-wang Chen, and Xiaotian Yu.
Recommended citation: Wang, Y., Xiong, C., Ju, C., Yang, G., Chen, Y. W., & Yu, X. (2024). A Deep Transfer Operator Learning Method for Temperature Field Reconstruction in a Lithium-Ion Battery Pack. IEEE Transactions on Industrial Informatics. https://ieeexplore.ieee.org/abstract/document/10462637
Accelerating Neural ODEs: A Variational Formulation-based Approach
Published in The Thirteenth International Conference on Learning Representations (ICLR), 2025
Hongjue Zhao, Yuchen Wang, Hairong Qi, Zijie Huang, Han Zhao, Lui Sha, and Huajie Shao.
Recommended citation: Zhao, H., Wang, Y., Qi, H., Huang, Z., Zhao, H., Sha, L., & Shao, H. (2025). Accelerating Neural ODEs A Variational Formulation-based Approach. In The Thirteenth International Conference on Learning Representations. https://openreview.net/forum?id=trV41CpAK4
A Generalizable Physics-Enhanced State Space Model for Long-Term Dynamics Forecasting in Complex Environments
Published in Proceedings of the 42nd International Conference on Machine Learning (ICML), 2025
Yuchen Wang, Hongjue Zhao, Haohong Lin, Enze Xu, Lifang He, and Huajie Shao.
Recommended citation: Yuchen Wang, Hongjue Zhao, Haohong Lin, Enze Xu, Lifang He, & Huajie Shao. (2025). A Generalizable Physics-Enhanced State Space Model for Long-Term Dynamics Forecasting in Complex Environments. Proceedings of the 42nd International Conference on Machine Learning (ICML). https://icml.cc/virtual/2025/poster/46230
A Generalizable Physics-guided Causal Model for Trajectory Prediction in Autonomous Driving
Published in 2026 IEEE International Conference on Robotics and Automation (ICRA);, 2026
Zhenyu Zong, Yuchen Wang, Haohong Lin, Lu Gan, and Huajie Shao.
Recommended citation: Zong, Z., Wang, Y., Lin, H., Gan, L., & Shao, H. (2026). A Generalizable Physics-guided Causal Model for Trajectory Prediction in Autonomous Driving. arXiv preprint arXiv:2602.13936. https://arxiv.org/abs/2602.13936
WestWorld: A Knowledge-Encoded Scalable Trajectory World Model for Diverse Robotic Systems
Published in International Conference on Machine Learning (ICML), 2026
Yuchen Wang*, Jiangtao Kong*, Sizhe Wei, Xiaochang Li, Haohong Lin, Hongjue Zhao, Tianyi Zhou, Lu Gan, and Huajie Shao.
Recommended citation: Wang, Y., Kong, J., Wei, S., Li, X., Lin, H., Zhao, H., Zhou, T., Gan, L., & Shao, H. (2026). WestWorld: A Knowledge-Encoded Scalable Trajectory World Model for Diverse Robotic Systems. International Conference on Machine Learning (ICML). https://arxiv.org/abs/2603.14392
Lens: A Knowledge-Guided Foundation Model for Network Traffic
Published in Transactions on Machine Learning Research (TMLR), 2026
Xiaochang Li, Chen Qian, Qineng Wang, Jiangtao Kong, Yuchen Wang, Ziyu Yao, Bo Ji, Long Cheng, Gang Zhou, and Huajie Shao.
Recommended citation: Li, X., Qian, C., Wang, Q., Kong, J., Wang, Y., Yao, Z., Ji, B., Cheng, L., Zhou, G., & Shao, H. (2026). Lens: A Knowledge-Guided Foundation Model for Network Traffic. Transactions on Machine Learning Research (TMLR). https://openreview.net/forum?id=cGDwTgnJIR
talks
Talk 1 on Relevant Topic in Your Field
Published:
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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