EAGER: Machine Learning and Data Assimilation for Discovery of Generalized Fokker-Planck Equation for Radiation Belt Modeling

University Of California-Los Angeles, Los Angeles CA

Investigators

Abstract

This project supports a two-year investigation into a better understanding and prediction of the extreme dynamic regions of space known as the Van Allen radiation belts. The near-Earth radiation environment is filled with high-energy particles trapped by the Earth's magnetic field. The particle intensity and distribution change as part of a much larger space weather system driven by the Sun. The radiation belts can be hazardous for satellites and astronauts in space. Therefore, it is crucial to be able to predict these energetic particles better for our space exploration. The new investigation is expected to lead to a more accurate understanding of the radiation belts using a novel combination of data assimilation and machine learning methods to discover generalized Fokker-Planck equations for radiation belt modeling. The Principal Investigator (PI) will use University of California-Los Angeles (UCLA)'s Versatile Electron Radiation Belt (VERB) model and data assimilation to learn and incorporate additional dynamical terms that would describe nonlinear wave-particle kinetic effects. The project will promote and pave the way for novel use of machine learning and data assimilation tools to discover missing physics in high-dimensional and complex space physics models. The initial implementation and vetting of combined data assimilation and machine learning algorithms in the radiation belt diffusion model will not only lead to significant advances in space weather modeling capabilities. Still, they will also have substantial broader impacts on space plasma physics and other disciplines. The project will thus have the potential to drastically change our understanding of radiation belt modeling, and advanced tools can find broader use for Earth's climate studies and engineering. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.