Enrico Camporeale, Space Weather Prediction Center, National Oceanic and Atmospheric Administration
Using Physics-Informed Neural Networks for solving inverse problems: a space physics case study
We use the framework of Physics-Informed Neural Network (PINN) to solve the inverse problem associated with the Fokker-Planck equation for radiation belts' electron transport, using 4 years of Van Allen Probes data. Traditionally, reduced models have employed a diffusion equation based on the quasilinear approximation. We show that the dynamics of 鈥渒iller electrons鈥� is described more accurately by a drift-diffusion equation, and that drift is as important as diffusion for nearly-equatorially trapped 鈭�1 MeV electrons in the inner part of the belt. Moreover, we present a recipe for gleaning physical insight from solving the ill-posed inverse problem of inferring model coefficients from data using PINNs. Furthermore, we derive a parameterization for the diffusion and drift coefficients as a function of L only, which is both simpler and more accurate than earlier models. Finally, we use the PINN technique to develop an automatic event identification method that allows identifying times at which the radial transport assumption is inadequate to describe all the physics of interest.