2022 review of data-driven plasma science
Data-driven science and technology offer transformative tools and methods to science. This
review article highlights the latest development and progress in the interdisciplinary field of …
review article highlights the latest development and progress in the interdisciplinary field of …
Cost function for low-dimensional manifold topology assessment
In reduced-order modeling, complex systems that exhibit high state-space dimensionality
are described and evolved using a small number of parameters. These parameters can be …
are described and evolved using a small number of parameters. These parameters can be …
Two-temperature thermochemical nonequilibrium model based on the coarse-grained treatment of molecular vibrational states
J Lv, Q Hong, X Wang, Y Huang, Q Sun - Physical Review E, 2024 - APS
Although the high-fidelity state-to-state (StS) model accurately describes high-temperature
thermochemical nonequilibrium flows, its practical application is hindered by the …
thermochemical nonequilibrium flows, its practical application is hindered by the …
Detection and prediction of equilibrium states in kinetic plasma simulations via mode tracking using reduced-order dynamic mode decomposition
A dynamic mode decomposition (DMD) based reduced-order model (ROM) is developed for
tracking, detection, and prediction of kinetic plasma behavior. DMD is applied to the high …
tracking, detection, and prediction of kinetic plasma behavior. DMD is applied to the high …
Koopman Autoencoders for Reduced‐Order Modeling of Kinetic Plasmas
Electromagnetic particle‐in‐cell (EMPIC) algorithms have been a popular choice for
simulating kinetic plasmas due to their ability to accurately capture complex transient …
simulating kinetic plasmas due to their ability to accurately capture complex transient …
[HTML][HTML] Feature extraction and reduced-order modelling of nitrogen plasma models using principal component analysis
Principal component analysis has been presented in recent research as an accurate and
efficient method to reduce the complex chemistry and kinetics of large reacting mechanisms …
efficient method to reduce the complex chemistry and kinetics of large reacting mechanisms …
A predictive physics-aware hybrid reduced order model for reacting flows
In this work, a new hybrid predictive Reduced Order Model (ROM) is proposed to solve
reacting flow problems. This algorithm is based on a dimensionality reduction using Proper …
reacting flow problems. This algorithm is based on a dimensionality reduction using Proper …
Development of skeletal kinetics mechanisms for plasma-assisted combustion via principal component analysis
The positive effect of plasma discharges on ignition and flame stability motivates the
development of detailed kinetic mechanisms for high-fidelity simulations of plasma-assisted …
development of detailed kinetic mechanisms for high-fidelity simulations of plasma-assisted …
A novel machine learning based lumping approach for the reduction of large kinetic mechanisms for plasma-assisted combustion applications
The development of skeletal mechanisms has become essential for multi-dimensional
simulations of plasma-assisted combustion (PAC). However, reduction tools developed for …
simulations of plasma-assisted combustion (PAC). However, reduction tools developed for …
[HTML][HTML] Hierarchical higher-order dynamic mode decomposition for clustering and feature selection
This article introduces a novel, fully data-driven method for forming reduced order models
(ROMs) in complex flow databases that consist of a large number of variables. The algorithm …
(ROMs) in complex flow databases that consist of a large number of variables. The algorithm …