麻豆av

教师入口
麻豆av

师资队伍

张瑞

您当前所在的位置： 麻豆av> 师资队伍> 专任教师> 张瑞
麻豆av

张瑞 讲师

返回列表

邮箱：[email protected]

个人网页：//optray.github.io/



张瑞，现任麻豆av 讲师，青年英才。于2024年获得中国科麻豆av 数学与系统科学研究院理学博士学位。2024年至2026年在麻豆av 从事博士后研究工作。主要研究方向为智能科学计算，关注物理规律与人工智能的深度融合，以及面向复杂物理系统的模拟、反演、控制与优化，相关成果应用于汽车与飞行器优化设计、极端天气预警与防灾减灾、油气能源勘探等重要场景。主持国家自然科学基金青年项目及多项国家重点研发计划子课题，累计负责科研经费 400 余万元。作为第一或通讯作者，已在 National Science Review、IEEE TPAMI、ICML、KDD、AAAI 等国际重要期刊与会议上发表论文十余篇。

教育经历

2019年-2024年，中国科麻豆av 数学与系统科学研究院，理学博士, 导师：马志明 院士

2015年-2019年，华东师范大学数学科学麻豆av ，理学学士


工作经历

2026年至今，麻豆av 讲师，青年英才

2024年至2026年，麻豆av 博士后，合作导师：孙浩


研究方向

物理驱动的深度学习方法及其工程应用

可解释人工智能


研究成果

Note: * equal contribution, # corresponding author

Preprint

[16] Mao, R.*, Zhang, R.*, Bai, X., Wu, T., Zhang, T., Chen, Z., Lin, M., Zeng, B., Xu, Y., Xiang, Y., Zhang, H., Goswami, S., Dawe, P. A., Xu, Y., An, Z., Yan, M., Lu, X., Wang, Y., Bai, R., Gao, H., Fang, X., Li, H., Sun, H.#, & Chen, Z. X.# (2026). Benchmarking Neural Surrogates on Realistic Spatiotemporal Multiphysics Flows. arXiv preprint arXiv:2512.18595.

[15] Zhang, R., Wan, H., Liu, Y., & Sun, H.# (2026). Stable Spectral Neural Operator for Learning Stiff PDE Systems from Limited Data. arXiv preprint arXiv:2512.11686.

[14] Zhang, R., Meng, Q., Wan, H., Liu, Y., Ma, Z.-M., & Sun, H. (2026). OmniFluids: Unified Physics Pre-trained Modeling of Fluid Dynamics. arXiv preprint arXiv:2506.10862.

2026

[13] Zeng, B.*, Zhang, R.*, Mao, R., Yan, M., Bai, X., Liu, Y., Chen, Z. X., Sun, H.# (2026). NPSolver: Neural Poisson Solver with Iterative Physics Supervision. SIGKDD Conference on Knowledge Discovery and Data Mining (KDD).

[12] Wan, H., Zhang, R.#, & Sun, H. (2026). Spectral-inspired Operator Learning with Limited Data and Unknown Physics. SIGKDD Conference on Knowledge Discovery and Data Mining (KDD).

[11] Zhou, H., Zhang, R.#, Wan, H., & Sun, H.# (2026). PerFlow: Physics-Embedded Rectified Flow for Efficient Reconstruction and Uncertainty Quantification of Spatiotemporal Dynamics. International Joint Conference on Artificial Intelligence (IJCAI).

[10] Sun, G.*[本科生], Mian, T.*[本科生], Huang, H.[本科生], Chen, H., Wan, H., Zhang, R.#, & Sun, H.# (2026). Geometry-Aware Neural Optimizer for Shape Optimization and Inversion. International Conference on Machine Learning (ICML).

[9] Wan, H.*, Wang, Q.*, Mi, Y., Zhang, R.#, & Sun, H.# (2026). PIMRL: Physics-Informed Multi-Scale Recurrent Learning for Burst-sampled Spatiotemporal Dynamics. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI).

[8] Du, X.*#, Dou, Q.*, Fan, L., & Zhang, R.# (2026). Flexible Concept Bottleneck Model. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI).

2025

[7] Wan, H.*, Zhang, R.*, Wang, Q., Liu, Y., & Sun, H. (2025). PeSANet: Physics-encoded Spectral Attention Network for Simulating PDE-Governed Complex Systems. International Joint Conference on Artificial Intelligence (IJCAI).

[6] Zhang, R., Meng, Q., Zhu, R., Wang, Y., Shi, W., Zhang, S., Ma, Z.-M., & Liu, T.-Y. (2025). Monte Carlo Neural PDE Solver for Learning PDEs via Probabilistic Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI).

[5] Zhang, R.*, Du, X.*, Yan, J., & Zhang, S. (2025). The Decoupling Concept Bottleneck Model. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI).

2024

[4] Zhang, R., Meng, Q., & Ma, Z.-M. (2024). Deciphering and Integrating Invariants for Neural Operator Learning with Various Physical Mechanisms. National Science Review (NSR).

2022

[3] Zhang, R., Hu, P., Meng, Q., Wang, Y., Zhu, R., Chen, B., Ma, Z.-M., & Liu, T.-Y. (2022). DRVN (Deep Random Vortex Network): A New Physics-informed Machine Learning Method for Simulating and Inferring Incompressible Fluid Flows. Physics of Fluids (POF).

[2] Zhang, R., & Zhang, S. (2022). Rethinking Influence Functions of Neural Networks in the Over-parameterized Regime. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI, Oral).

[1] Du, X., Yan, J., Zhang, R., & Zha, H. (2022). Cross-network Skip-gram Embedding for Joint Network Alignment and Link Prediction. IEEE Transactions on Knowledge and Data Engineering (TKDE).


基金与资助

  • 国家管网合作项目（主持，200万元）2026.01 - 2027.05
  • 国家重点研发计划（项目骨干，负责经费100万元）2026.01 - 2028.12
  • 国家重点研发计划颠覆性项目（项目骨干，负责经费20万元）2026.01 - 2027.12
  • 国家自然科学基金青年科学基金项目（主持，30万元）2026.01 - 2028.12
  • 国家资助博士后研究人员计划B档（36万元）2025.09 - 2026.06
  • 中国博士后科学基金第77批面上资助（主持，8万元）2025.09 - 2026.06

荣誉奖励

麻豆av 青年英才

中科院数麻豆av 院长奖学金特等奖

华罗庚奖学金

上海市优秀毕业生


社会兼职

期刊审稿人： IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI), IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), IEEE Transactions on Services Computing (IEEE TSC), Transactions on Machine Learning Research (TMLR), ACM Transactions on AI for Science (ACM TAIS), Machine Intelligence Research (MIR), Physics of Fluids (POF)


会议审稿人：Neural Information Processing Systems (NeurIPS), International Conference on Learning Representations (ICLR), International Conference on Machine Learning (ICML), Knowledge Discovery and Data Mining (KDD), AAAI Conference on Artificial Intelligence (AAAI)