Physics-informed neural networks (PINNs) represent a significant advancement at the
intersection of machine learning and physical sciences, offering a powerful framework for …

Abstract The Near-Earth Space Radiation and Plasma Environment falls within the realm of
G3 Cluster (G3 refers to 'Near-Earth Radiation and Plasma Environment'of the 'Coupled …

Physics-informed neural networks (PINNs) are machine learning models that integrate data-
based learning with partial differential equations (PDEs). In this work, for the first time we …

Purely data‐driven ionospheric modeling fails to adequately obey fundamental physical
laws. To overcome this shortcoming, we propose a novel ionospheric inversion model …

Quasilinear theories have been shown to well describe a range of transport phenomena in
magnetospheric, space, astrophysical and laboratory plasma “weak turbulence” scenarios. It …

In the Earth's radiation belts, an upper limit on the electron flux is expected to be imposed by
the Kennel‐Petschek mechanism, through the generation of exponentially more intense …

We screen several algorithms for their ability to produce good predictive models of hourly 40–
150 keV electron flux at geostationary orbit (data from GOES‐13) using solar wind …

The dynamics of the outer radiation belt are traditionally associated with wave‐particle
resonant interactions, which provide local electron acceleration and losses through very low …

Electron resonant interaction with ultra‐low‐frequency (ULF) waves is considered to be a
driver of electron radial transport in Earth's inner magnetosphere. Traditional concept of …

Predicting the process of porosity-based ductile damage in polycrystalline metallic materials
is an essential practical topic. Ductile damage and its precursors are represented by …