Unsupervised Discovery of Non-Linear Plasma Physics using Differentiable Kinetic Simulations

Plasma supports collective modes and particle-wave interactions that leads to complex behavior in, for example, inertial fusion energy applications. While plasma can sometimes be modeled as a charged fluid, a kinetic description is often crucial for studying nonlinear effects in the higher dimensional momentum-position phase-space that describes the full complexity of plasma dynamics. We create a differentiable solver for the 3D partial-differential-equation describing the plasma kinetics and introduce a domain-specific objective function. Using this framework, we perform gradient-based optimization of neural networks that provide forcing function parameters to the differentiable solver given a set of initial conditions. We apply this to an inertial-fusion relevant configuration and find that the optimization process exploits a novel physical effect.