Found 2 entries in the Bibliography.

Showing entries from 1 through 2


Application and testing of the L * neural network with the self-consistent magnetic field model of RAM-SCB

We expanded our previous work on L* neural networks that used empirical magnetic field models as the underlying models by applying and extending our technique to drift shells calculated from a physics-based magnetic field model. While empirical magnetic field models represent an average, statistical magnetospheric state, the RAM-SCB model, a first-principles magnetically self-consistent code, computes magnetic fields based on fundamental equations of plasma physics. Unlike the previous L* neural networks that include McIlwai ...

Yu, Yiqun; Koller, Josef; Jordanova, Vania; Zaharia, Sorin; Friedel, Reinhard; Morley, Steven; Chen, Yue; Baker, Daniel; Reeves, Geoffrey; Spence, Harlan;

YEAR: 2014     DOI: 10.1002/jgra.v119.310.1002/2013JA019350

Van Allen Probes


Modeling ring current ion and electron dynamics and plasma instabilities during a high-speed stream driven storm

1] The temporal and spatial development of the ring current is evaluated during the 23\textendash26 October 2002 high-speed stream (HSS) storm, using a kinetic ring current-atmosphere interactions model with self-consistent magnetic field (RAM-SCB). The effects of nondipolar magnetic field configuration are investigated on both ring current ion and electron dynamics. As the self-consistent magnetic field is depressed at large (>4RE) radial distances on the nightside during the storm main phase, the particles\textquoteright d ...

Jordanova, V.; Welling, D.; Zaharia, S.; Chen, L.; Thorne, R.;

YEAR: 2012     DOI: 10.1029/2011JA017433