Data‐Driven Discovery of Fokker‐Planck Equation for the Earth's Radiation Belts Electrons Using Physics‐Informed Neural Networks

Camporeale, E.; Wilkie, George J.; Drozdov, Alexander Y.; Bortnik, Jacob

doi:10.1029/2022ja030377

i

Data‐Driven Discovery of Fokker‐Planck Equation for the Earth's Radiation Belts Electrons Using Physics‐Informed Neural Networks

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2022
By Camporeale, E. ; Wilkie, George J. ; Drozdov, Alexander Y. ; ...

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Journal Title:

Journal of Geophysical Research: Space Physics
Personal Author:

Camporeale, E. ; Wilkie, George J. ; Drozdov, Alexander Y. ; Bortnik, Jacob
NOAA Program & Office:

NWS (National Weather Service) ; CIRES (Cooperative Institute for Research in Environmental Sciences) ; SWPC (Space Weather Prediction Center)
Description:

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 “killer electrons” is described more accurately by a drift-diffusion equation, and that drift is as important as diffusion for nearly-equatorially trapped ∼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
Keywords:

Geophysics Space And Planetary Science Space And Planetary Science Space And Planetary Science
Source:

Journal of Geophysical Research: Space Physics, 127(7)
DOI:

https://doi.org/10.1029/2022ja030377
ISSN:

2169-9380 ; 2169-9402
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PDF
Publisher:

American Geophysical Union (AGU)
Document Type:

Journal Article
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urn:sha256:e53c394850eab19fcf8c52d7ab78a6a19494c8218cdcb193efd626230f9a7351
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https://repository.library.noaa.gov/view/noaa/52762/noaa_52762_DS1.pdf
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