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 …
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 …
[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 …
Z Zhang, F Zhang, W Gong, T Chen, L Tan… - Physica D: Nonlinear …, 2024 - Elsevier
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 …
Physics informed Neural Networks applied to the description of wave-particle resonance in kinetic simulations of fusion plasmas
J Kumar, D Zarzoso, V Grandgirard, J Ebert… - arXiv preprint arXiv …, 2023 - arxiv.org
The Vlasov-Poisson system is employed in its reduced form version (1D1V) as a test bed for
the applicability of Physics Informed Neural Network (PINN) to the wave-particle resonance …
the applicability of Physics Informed Neural Network (PINN) to the wave-particle resonance …
Data-driven discovery of a heat flux closure for electrostatic plasma phenomena
ER Ingelsten, MC McGrae-Menge, EP Alves… - arXiv preprint arXiv …, 2024 - arxiv.org
Progress in understanding multi-scale collisionless plasma phenomena requires employing
tools which balance computational efficiency and physics fidelity. Collisionless fluid models …
tools which balance computational efficiency and physics fidelity. Collisionless fluid models …
Data-Driven Modeling of Landau Damping by Fourier Neural Operator
The development of machine learning techniques enables us to discover partial differential
equations from sparse data, which has important implications for modeling complex physical …
equations from sparse data, which has important implications for modeling complex physical …