21 citations · 41 across the 3 of their papers we have counts for
3 papers
physics.plasm-ph2023
Data-Driven Modeling of Landau Damping by Fourier Neural Operator
Shichen Wei, Yuhong Liu, Haiyang Fu +2
The development of machine learning techniques enables us to construct surrogate models from data of direct numerical simulations, which has important implications for modeling com…
physics.plasm-ph2022★ 20 cited
Data-driven modeling of Landau damping by physics-informed neural networks
Yilan Qin, Jiayu Ma, Mingle Jiang +5
Kinetic approaches are generally accurate in dealing with microscale plasma physics problems but are computationally expensive for large-scale or multiscale systems. One of the lon…
physics.plasm-ph2022★ 21 cited
Data-driven, multi-moment fluid modeling of Landau damping
Wenjie Cheng, Haiyang Fu, Liang Wang +6
Deriving governing equations of complex physical systems based on first principles can be quite challenging when there are certain unknown terms and hidden physical mechanisms in t…