  The development of machine learning techniques enables us to construct
surrogate models from data of direct numerical simulations, which has important
implications for modeling complex physical systems. In this paper, based on the
output from 1D Vlasov-Ampere simulations, we adopt the Fourier Neural Operator
(FNO) to build surrogate models of Landau fluid closure for multi-moment fluid
equations from kinetic simulation data. The trained FNO is able to obtain the
heat flux using electron density as input, in agreement with the true value
from kinetic simulations. We compare the physical quantities obtained using the
FNO and Multilayer Perceptron (MLP) architectures, and found that the results
of FNO are significantly better than that of MLP.

Plasma Physics;Computational Physics;Space Physics