Data-driven construction of a generalized kinetic collision operator from molecular dynamics
This work addresses limitations in plasma physics modeling for scenarios with significant correlations, representing an incremental improvement over existing methods.
The researchers tackled the problem of accurately modeling plasma kinetics by developing a data-driven approach to learn a generalized kinetic collision operator from molecular dynamics, which accounts for anisotropic energy transfer and shows improved predictions over the Landau model in cases with non-negligible correlations.
We introduce a data-driven approach to learn a generalized kinetic collision operator directly from molecular dynamics. Unlike the conventional (e.g., Landau) models, the present operator takes an anisotropic form that accounts for a second energy transfer arising from the collective interactions between the pair of collision particles and the environment. Numerical results show that preserving the broadly overlooked anisotropic nature of the collision energy transfer is crucial for predicting the plasma kinetics with non-negligible correlations, where the Landau model shows limitations.