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A novel ionospheric inversion model: PINN‐SAMI3 (physics informed neural network based on SAMI3)
Purely data‐driven ionospheric modeling fails to adequately obey fundamental physical
laws. To overcome this shortcoming, we propose a novel ionospheric inversion model …
laws. To overcome this shortcoming, we propose a novel ionospheric inversion model …
EMWP-RNN: A physics-encoded recurrent neural network for wave propagation in plasmas
Electromagnetic (EM) wave propagation and inversion in complex time-varying medium is a
challenging problem, particularly for plasma applications. We extend the EM wave–plasma …
challenging problem, particularly for plasma applications. We extend the EM wave–plasma …
[HTML][HTML] Numerical study of magnetic island coalescence using magnetohydrodynamics with adaptively embedded particle-in-cell model
Collisionless magnetic reconnection typically requires kinetic treatment that is, in general,
computationally expensive compared to fluid-based models. In this study, we use the …
computationally expensive compared to fluid-based models. In this study, we use the …
Prediction of spatiotemporal dynamics using deep learning: Coupled neural networks of long short-terms memory, auto-encoder and physics-informed neural …
Several classic reaction-diffusion models using partial differential equations (PDEs) have
been established to elucidate the formation mechanism of vegetation patterns. However …
been established to elucidate the formation mechanism of vegetation patterns. However …