In Search of a Data Driven Symbolic Multi-Fluid 10-Moment Model Closure

Abstract: The inclusion of kinetic effects into fluid models has been a long standing problem in magnetic reconnection and plasma physics. Generally the pressure tensor is reduced to a scalar which is an approximation used to aid in the modeling of large scale global systems such as the Earth's magnetosphere. This unfortunately omits important kinetic physics which have been shown to play a crucial role in collisionless regimes. The multi-fluid 10-moment model on the other-hand retains the full symmetric pressure tensor. The 10-moment model is constructed by taking moments of the Vlasov equation up to second order, and includes the scalar density, the vector bulk-flow, and the symmetric pressure tensor for a total of 10 separate components. Use of the multi-fluid 10-moment model requires a closure which truncates the cascading system of equations. Here we look to leverage data-driven methodologies to seek a closure which may improve physical fidelity of the 10-moment multi-fluid model in collisionless regimes. Specifically we use the Sparse Identification of Nonlinear Dynamics (SINDy) method for symbolic equation discovery to seek the truncating closure from fully kinetic particle-in-cell simulation data, which inherently retains the relevant kinetic physics. We verify our method by reproducing the 10-moment model from the PIC particle data and use the method to generate a closure truncating the 10-moment model which is analyzed through the nonlinear phase of reconnection.