Data-driven discovery of Fokker-Planck equation for the Earth's radiation belts electrons using Physics-Informed Neural Networks | CU Experts

We use the framework of Physics-Informed Neural Network (PINN) to solve; the inverse problem associated to the Fokker-Planck equation for; radiation belts’ electron transport, using four 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 “killer electrons’ is described more accurately by a; drift-diffusion equation, and that drift is as important as diffusion; for nearly-equatorially trapped $sim$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 to identify; times at which the radial transport assumption is inadequate to describe; all the physics of interest.

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