Machine learning of hidden variables in multiscale fluid simulation

AS Joglekar, AGR Thomas - Machine Learning: Science and …, 2023 - iopscience.iop.org
Solving fluid dynamics equations often requires the use of closure relations that account for
missing microphysics. For example, when solving equations related to fluid dynamics for …

EMWP-RNN: A Physics-Encoded Recurrent Neural Network for Wave Propagation in Plasmas

Y Qin, H Fu, F Xu, Y Jin - IEEE Antennas and Wireless …, 2023 - ieeexplore.ieee.org
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 …

Data-driven modeling of Landau damping by physics-informed neural networks

Y Qin, J Ma, M Jiang, C Dong, H Fu, L Wang… - Physical Review …, 2023 - APS
Kinetic approaches are generally accurate in dealing with microscale plasma physics
problems but are computationally expensive for large-scale or multiscale systems. One of …

A novel ionospheric inversion model: PINN‐SAMI3 (physics informed neural network based on SAMI3)

J Ma, H Fu, JD Huba, Y Jin - Space Weather, 2024 - Wiley Online Library
Purely data‐driven ionospheric modeling fails to adequately obey fundamental physical
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

D Li, Y Chen, C Dong, L Wang, G Toth - AIP Advances, 2023 - pubs.aip.org
Collisionless magnetic reconnection typically requires kinetic treatment that is, in general,
computationally expensive compared to fluid-based models. In this study, we use the …

Fast forward modeling of magnetotelluric data in complex continuous media using an extended Fourier DeepONet architecture

W Liao, R Peng, X Hu, Y Zhang, W Zhou, X Fu, H Lin - Geophysics, 2024 - library.seg.org
The calculations of magnetotelluric (MT) responses play a fundamental role in the inversion
and resolution analysis for MT problems. Conventional numerical methods for forward …

Electron cyclotron drift instability and anomalous transport: two-fluid moment theory and modeling

L Wang, A Hakim, J Juno… - Plasma Sources Science …, 2022 - iopscience.iop.org
In the presence of a strong electric field perpendicular to the magnetic field, the electron
cross-field (E× B) flow relative to the unmagnetized ions can cause the so-called electron …

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 …

Data-driven Modeling of Plasma Fluid Closure and Parameter Prediction

K Liu, W Cheng, H Fu, M Jiang, J Ma… - 2022 International …, 2022 - ieeexplore.ieee.org
The advances of technology enable us to discover partial differential equations (PDEs) from
data of a certain size, which is of great significance for modeling physical systems. In this …

Data-Driven Modeling of Landau Damping by Fourier Neural Operator

S Wei, Y Liu, H Fu, C Dong… - 2023 International Applied …, 2023 - ieeexplore.ieee.org
The development of machine learning techniques enables us to discover partial differential
equations from sparse data, which has important implications for modeling complex physical …