* highlights student work
** highlights postdoc/scientist work
S. Shekarpaz**, C. F. Dong, L. Wang**, Modeling Shock-Driven MHD Turbulence with Physics-Informed Neural Networks, to be submitted, 2025.
S. Shekarpaz**, C. F. Dong, Z. Huang**, L. Wang**, Operator Learning Surrogate for Solving the Vlasov Equation Using Deep Operator Networks, to be submitted, 2025.
C. Zhang**, C. F. Dong, H. Zhou**, X. Li**, J. Gao**, L. Wang**, Y. Qin*, Y. Dong, H. Nilsson, J. S. Halekas, G. Hanley, E. Dubinin, J. G. Luhmann, S. Curry, Deep-Learning Identification and Global Characterization of Energetic O₂⁺ Beams at Mars, submitted.
H. Geng, Z. Huang, H. Li, W. Li, K. Wu, Z. Zhou, Y. Pang, W. Liu, Z. Xu, Z. Li, Z. Zhang, C. F. Dong, J. Sun, T. Zheng, F. Xie, Y. Ma, Y. Shi, J. Wang, T. Xie, Z. Di, X. Liu, Q. Gao, Y. Liu, J. Pan, S. Huang, X.-H. Ma, L. Yuan, Z. Zhu, Z. Liu, Z. Xu, K. Liang, J. Xian, Z. Zhao, T. Ma, F. Chang, Y. Hu, L. Xu, X. Tang, J. Xie, P. Zhang, Q. Gao, C. Xing, Z. Zhao, X. Wang, L. Wang, F. Wu, Y. Xing, X. Meng, Z. Yin, Y. Wu, L. Yang, ScienceIDE: Turning World’s Scientific Codebase into Agent Learnable Environments, submitted. arXiv:2609.19134
B. Xia*, C. F. Dong, B. Li**, Y. Qin*, Z. Xie**, S. H. Son*, A. Stanier, J. Yoo, H. Ji, A. Diallo, H. Ding*, J. H. Wise, Toward a Multimodal Foundation Model for Plasma State Reconstruction, NeurIPS 2026 Workshop on Physical World AI (PhysWorldAI), (2026). arXiv:2610.15076
B. Xia*, C. F. Dong, B. Li**, Y. Qin*, Z. Xie**, S. H. Son*, A. Stanier, J. Yoo, H. Ji, A. Diallo, H. Ding*, J. H. Wise, Toward a Multimodal Foundation Model for Plasma State Reconstruction, NeurIPS 2026 Workshop on Physical World AI (PhysWorldAI), (2026).
Y. Qin*, C. F. Dong, H. Zhou**, C. Zhang**, K. Xu*, J. Gao**, S. Shekarpaz**, X. Li**, L. Wang**, Automatic Classification of Plasma Regions at Mars Using Machine Learning, submitted, 2026. arXiv:2604.17131
J. Gao**, C. F. Dong, C. Zhang**, Y. Qin*, S. Shekarpaz**, X. Li**, L. Wang**, H. Zhou**, A. Tadlock*, Physics-Informed Neural Networks for Modeling the Martian Induced Magnetosphere, Geophys. Res. Lett. 53, e2025GL121532 (2026). arXiv:2512.16175
Y. Liu*, H. Fu, L. Wang**, C. F. Dong, S. Wei, Y. Xi, C. Shang, Y. Qin*, Data-driven modeling of electrostatic turbulence by physics-informed Fourier neural operator, Mach. Learn.: Sci. Technol. 6, 045050 (2025)
S. Shekarpaz**, C. F. Dong, Z. Huang**, Surrogate Modeling of Landau Damping with Deep Operator Networks, ApJ 990, 161 (2025). arXiv:2507.16960
Z. Huang**, C. F. Dong, L. Wang**, Machine-learning heat flux closure for multi-moment fluid modeling of nonlinear Landau damping, Proceedings of the National Academy of Sciences 122, e2419073122 (2025). arXiv:2503.11090
S. C. Wei*, Y. H. Liu*, H. Y. Fu, C. F. Dong, L. Wang, Data-Driven Modeling of Landau Damping by Fourier Neural Operator, 2023 International Applied Computational Electromagnetics Symposium (ACES), Hangzhou, China, pp. 01-03 (2023). arXiv:2308.02972
Y. Qin*, J. Ma*, M. Jiang*, C. F. Dong, H. Fu, L. Wang, W. Cheng, and Y. Jin, Data-driven modeling of Landau damping by physics-informed neural networks, Phys. Rev. Research 5, 033079 (2023). arXiv:2211.01021
W. J. Cheng*, H. Y. Fu, L. Wang, C. F. Dong, Y. Q. Jin, M. L. Jiang, J. Y. Ma, Y. L. Qin, and K. X. Liu, Data-driven, multi-moment fluid modeling of Landau damping, Computer Physics Communications 282, 108538 (2023). arXiv:2209.04726.