[HTML][HTML] Scientific machine learning through physics–informed neural networks: Where we are and what's next
Abstract Physics-Informed Neural Networks (PINN) are neural networks (NNs) that encode
model equations, like Partial Differential Equations (PDE), as a component of the neural …
model equations, like Partial Differential Equations (PDE), as a component of the neural …
[HTML][HTML] A review of physics-informed machine learning in fluid mechanics
Physics-informed machine-learning (PIML) enables the integration of domain knowledge
with machine learning (ML) algorithms, which results in higher data efficiency and more …
with machine learning (ML) algorithms, which results in higher data efficiency and more …
A point-cloud deep learning framework for prediction of fluid flow fields on irregular geometries
We present a novel deep learning framework for flow field predictions in irregular domains
when the solution is a function of the geometry of either the domain or objects inside the …
when the solution is a function of the geometry of either the domain or objects inside the …
Turbulence closure for high Reynolds number airfoil flows by deep neural networks
L Zhu, W Zhang, X Sun, Y Liu, X Yuan - Aerospace Science and …, 2021 - Elsevier
The combination of turbulence big data with artificial intelligence is an active research topic
for turbulence study. This work constructs black-box algebraic models to substitute the …
for turbulence study. This work constructs black-box algebraic models to substitute the …
Feature selection and processing of turbulence modeling based on an artificial neural network
Data-driven turbulence modeling has been considered an effective method for improving the
prediction accuracy of Reynolds-averaged Navier–Stokes equations. Related studies aimed …
prediction accuracy of Reynolds-averaged Navier–Stokes equations. Related studies aimed …
Ensemble Kalman method for learning turbulence models from indirect observation data
In this work, we propose using an ensemble Kalman method to learn a nonlinear eddy
viscosity model, represented as a tensor basis neural network, from velocity data. Data …
viscosity model, represented as a tensor basis neural network, from velocity data. Data …
Enhancing the shear-stress-transport turbulence model with symbolic regression: A generalizable and interpretable data-driven approach
Turbulence modeling within the Reynolds-averaged Navier-Stokes (RANS) equations'
framework is essential in engineering due to its high efficiency. Field-inversion and machine …
framework is essential in engineering due to its high efficiency. Field-inversion and machine …
Formulating turbulence closures using sparse regression with embedded form invariance
S Beetham, J Capecelatro - Physical Review Fluids, 2020 - APS
A data-driven framework for formulation of closures of the Reynolds-average Navier-Stokes
(RANS) equations is presented. In recent years, the scientific community has turned to …
(RANS) equations is presented. In recent years, the scientific community has turned to …
Wall model based on neural networks for LES of turbulent flows over periodic hills
In this work, a data-driven wall model for turbulent flows over periodic hills is developed
using the feedforward neural network (FNN) and data from wall-resolved large-eddy …
using the feedforward neural network (FNN) and data from wall-resolved large-eddy …
基于组合神经网络的雷诺平均湍流模型多次修正方法
张珍， 叶舒然， 岳杰顺， 王一伟， 黄晨光 - 力学学报, 2021 - lxxb.cstam.org.cn
求解雷诺平均(Reynolds-averaged Navier-Stokes, RANS) 方程依然是工程应用中有效且实用
的方法, 但对雷诺应力建模的不确定性会导致该方法的预测精度具有很大差异 …
的方法, 但对雷诺应力建模的不确定性会导致该方法的预测精度具有很大差异 …