☆ 4.2 Article

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

PHYSICAL REVIEW RESEARCH (2023)

相关参考文献

注意：仅列出部分参考文献，下载原文获取全部文献信息。

Article Nanoscience & Nanotechnology

Numerical study of magnetic island coalescence using magnetohydrodynamics with adaptively embedded particle-in-cell model

Dion Li et al.

Summary: Collisionless magnetic reconnection usually requires expensive kinetic treatment. In this study, we use an innovative MHD-AEPIC model to investigate the interaction of two magnetic flux ropes. By comparing the simulation results among three cases, we find that good agreement can be achieved by incorporating large adaptive PIC regions within the MHD domain, indicating the importance of the coupling between macro-scale MHD and micro-scale kinetic physics in the highly kinetic magnetic island coalescence problem.

AIP ADVANCES (2023)

添加到收藏夹

Article Astronomy & Astrophysics

Discovery of double Hall pattern associated with collisionless magnetic reconnection in dusty plasmas

Shu-Di Yang et al.

Summary: This article reports the first fully kinetic simulations of collisionless reconnection in a three-species dusty plasma with negatively charged dust grains, discovering the double Hall pattern and analyzing the reconnection rate. This study is significant for explaining observations of planetary magnetospheres and astrophysical objects, as well as for laboratory studies of dusty plasmas.

MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY (2023)

添加到收藏夹

Article Computer Science, Interdisciplinary Applications

Data-driven, multi-moment fluid modeling of Landau damping

Wenjie Cheng et al.

Summary: In this work, a deep learning architecture is applied to learn fluid partial differential equations (PDEs) of a plasma system, which can incorporate kinetic effects and reproduce physical quantities from a fully kinetic model.

COMPUTER PHYSICS COMMUNICATIONS (2023)

添加到收藏夹

Article Engineering, Electrical & Electronic

Physics-Informed Deep Neural Network for Inhomogeneous Magnetized Plasma Parameter Inversion

Yangyang Zhang et al.

Summary: This letter presents a physics-informed deep neural network method for the inversion of inhomogeneous plasma parameters and reconstruction of electromagnetic fields in the Maxwell's plasma coupling system. Simulation results demonstrate that this method can effectively achieve simultaneous inversion of inhomogeneous plasma parameters and global fields, and it has certain generalization ability.

IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS (2022)

添加到收藏夹

Article Engineering, Multidisciplinary

Gradient-enhanced physics-informed neural networks for forward and inverse PDE

Jeremy Yu et al.

Summary: The article introduces a new method called gradient-enhanced physics-informed neural networks (gPINNs) to improve the accuracy of PINNs. gPINNs leverage the gradient information of the PDE residual to enhance the loss function. Experimental results demonstrate that gPINNs outperform traditional PINN with fewer training points.

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2022)

添加到收藏夹

Article Physics, Fluids & Plasmas

Identification of high order closure terms from fully kinetic simulations using machine learning

B. Laperre et al.

Summary: Simulations of large-scale plasma systems often use a fluid approximation approach that lacks small-scale physical processes. This paper presents new closure terms constructed using machine learning techniques, which show promising results for capturing pressure tensor and heat flux in 2D magnetic reconnection simulations. The accuracy of the models is found to depend on the sampling of training points.

PHYSICS OF PLASMAS (2022)

添加到收藏夹

Article Physics, Multidisciplinary

Data-driven discovery of reduced plasma physics models from fully kinetic simulations

E. P. Alves et al.

Summary: This study reports the data-driven discovery of accurate reduced plasma models directly from first-principles particle-in-cell simulations, demonstrating the potential of this approach to accelerate the development of reduced theoretical models of complex nonlinear plasma phenomena and design computationally efficient algorithms for multiscale plasma simulations.

PHYSICAL REVIEW RESEARCH (2022)

添加到收藏夹

Article Computer Science, Interdisciplinary Applications

Physics-informed neural networks for solving forward and inverse flow problems via the Boltzmann-BGK formulation

Qin Lou et al.

Summary: In this study, physics-informed neural networks (PINNs) are utilized to solve forward and inverse problems via the Boltzmann-BGK formulation, enabling simulations of both continuous and rarefied flows. The PINN-BGK model, consisting of three sub-networks, successfully approximates various benchmark flows and infers flow fields in the entire computational domain through minimizing residuals and mismatches. Results show that PINN-BGK can accurately infer velocity fields in the entire domain and transfer learning can accelerate the training process significantly.

JOURNAL OF COMPUTATIONAL PHYSICS (2021)

添加到收藏夹

Article Mechanics

Flow over an espresso cup: inferring 3-D velocity and pressure fields from tomographic background oriented Schlieren via physics-informed neural networks

Shengze Cai et al.

Summary: This research proposes a new method based on physics-informed neural networks to infer full continuous three-dimensional velocity and pressure fields from Tomo-BOS imaging. Validation on synthetic and real data demonstrates the accuracy and feasibility of the method.

JOURNAL OF FLUID MECHANICS (2021)

添加到收藏夹

Article Physics, Multidisciplinary

Data-driven rogue waves and parameter discovery in the defocusing nonlinear Schrodinger equation with a potential using the PINN deep learning

Li Wang et al.

Summary: This paper uses the multi-layer PINN deep learning method to study data-driven rogue wave solutions of the defocusing nonlinear Schrodinger (NLS) equation with time-dependent potential. By considering various initial conditions and periodic boundary conditions, the study discusses learning parameters in the context of rogue wave solutions.

PHYSICS LETTERS A (2021)

添加到收藏夹

Article Physics, Fluids & Plasmas

Uncovering turbulent plasma dynamics via deep learning from partial observations

A. Mathews et al.

Summary: This study demonstrates a physics-informed deep learning framework that accurately learns turbulent fields consistent with the two-fluid theory, providing a new paradigm for advanced plasma diagnostics and validation of magnetized plasma turbulence theories in challenging thermonuclear environments.

PHYSICAL REVIEW E (2021)

添加到收藏夹

Article Multidisciplinary Sciences

Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations

Maziar Raissi et al.

SCIENCE (2020)

添加到收藏夹

Article Physics, Fluids & Plasmas

Machine learning surrogate models for Landau fluid closure

Chenhao Ma et al.

PHYSICS OF PLASMAS (2020)

添加到收藏夹

Article Computer Science, Interdisciplinary Applications

Exact and locally implicit source term solvers for multifluid-Maxwell systems

Liang Wang et al.

JOURNAL OF COMPUTATIONAL PHYSICS (2020)

添加到收藏夹

Article Physics, Multidisciplinary

Electron acceleration in laboratory-produced turbulent collisionless shocks

F. Fiuza et al.

NATURE PHYSICS (2020)

添加到收藏夹

Article Computer Science, Interdisciplinary Applications

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

M. Raissi et al.

JOURNAL OF COMPUTATIONAL PHYSICS (2019)

添加到收藏夹

Article Computer Science, Interdisciplinary Applications

A six-moment multi-fluid plasma model

Zhenguang Huang et al.

JOURNAL OF COMPUTATIONAL PHYSICS (2019)

添加到收藏夹

Article Geosciences, Multidisciplinary

Global Ten-Moment Multifluid Simulations of the Solar Wind Interaction with Mercury: From the Planetary Conducting Core to the Dynamic Magnetosphere

Chuanfei Dong et al.

GEOPHYSICAL RESEARCH LETTERS (2019)

添加到收藏夹

Article Computer Science, Interdisciplinary Applications

PDE-Net 2.0: Learning PDEs from data with a numeric-symbolic hybrid deep network

Zichao Long et al.

JOURNAL OF COMPUTATIONAL PHYSICS (2019)

添加到收藏夹

Article Physics, Fluids & Plasmas

An introductory guide to fluid models with anisotropic temperatures. Part 2. Kinetic theory, Pade approximants and Landau fluid closures

P. Hunana et al.

JOURNAL OF PLASMA PHYSICS (2019)

添加到收藏夹

Article Computer Science, Interdisciplinary Applications

Discontinuous Galerkin algorithms for fully kinetic plasmas

J. Juno et al.

JOURNAL OF COMPUTATIONAL PHYSICS (2018)

添加到收藏夹

Article Astronomy & Astrophysics

Electron Physics in 3-D Two-Fluid 10-Moment Modeling of Ganymede's Magnetosphere

Liang Wang et al.

JOURNAL OF GEOPHYSICAL RESEARCH-SPACE PHYSICS (2018)

添加到收藏夹

Article Physics, Nuclear

High flux femtosecond x-ray emission from the electron-hose instability in laser wakefield accelerators

C. F. Dong et al.

PHYSICAL REVIEW ACCELERATORS AND BEAMS (2018)

添加到收藏夹

Article Physics, Fluids & Plasmas

Relativistic-electron-driven magnetic reconnection in the laboratory

A. E. Raymond et al.

PHYSICAL REVIEW E (2018)

添加到收藏夹

Article Multidisciplinary Sciences

Data-driven discovery of partial differential equations

Samuel H. Rudy et al.

SCIENCE ADVANCES (2017)

添加到收藏夹

Article Multidisciplinary Sciences

Discovering governing equations from data by sparse identification of nonlinear dynamical systems

Steven L. Brunton et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2016)

添加到收藏夹

Article Multidisciplinary Sciences

Distilling Free-Form Natural Laws from Experimental Data

Michael Schmidt et al.

SCIENCE (2009)

添加到收藏夹

Article Nuclear Science & Technology

Extended MHD modelling with the ten-moment equations

Ammar H. Hakim

JOURNAL OF FUSION ENERGY (2008)

添加到收藏夹

Article Multidisciplinary Sciences

Automated reverse engineering of nonlinear dynamical systems

Josh Bongard et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2007)

添加到收藏夹

© Peeref 2019-2024. All rights reserved.