no code implementations • 2 Nov 2022 • Yilan Qin, Jiayu Ma, Mingle Jiang, Chuanfei Dong, Haiyang Fu, Liang Wang, Wenjie Cheng, YaQiu Jin
The multi-moment fluid model is trained with a small fraction of sparsely sampled data from kinetic simulations of Landau damping, using the physics-informed neural network (PINN) and the gradient-enhanced physics-informed neural network (gPINN).
no code implementations • 10 Sep 2022 • Wenjie Cheng, Haiyang Fu, Liang Wang, Chuanfei Dong, YaQiu Jin, Mingle Jiang, Jiayu Ma, Yilan Qin, Kexin Liu
The data-driven fluid modeling of PDEs for complex physical systems may be applied to improve fluid closure and reduce the computational cost of multi-scale modeling of global systems.
