菠菜平台-十大菠菜平台

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).