* highlights student work
** highlights postdoc/scientist work
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, submitted, 2025.
S. Shekarpaz**, C. F. Dong, Z. Huang**, Surrogate Modeling of Landau Damping with Deep Operator Networks, ApJ, in press (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.