Physical Sciences and Mathematics Commons^™

Application And Testing Of The L Neural Network With The Self-Consistent Magnetic Field Model Of Ram-Scb, Yiqun Yu, Josef Koller, Vania K. Jordanova, Sorin G. Zaharia, R. Friedel, S. K. Morley, Yue Chen, D. N. Baker, Geoffrey Reeves, Harlan E. Spence

Physics & Astronomy

Abstract

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 McIlwain L and mirror point magnetic field as part of the inputs, the new L neural network only requires solar wind conditions and the Dst index, allowing …