Promising directions of machine learning for partial differential equations
SL Brunton, JN Kutz - Nature Computational Science, 2024 - nature.com
Partial differential equations (PDEs) are among the most universal and parsimonious
descriptions of natural physical laws, capturing a rich variety of phenomenology and …
descriptions of natural physical laws, capturing a rich variety of phenomenology and …
Turbulence modeling in the age of data
Data from experiments and direct simulations of turbulence have historically been used to
calibrate simple engineering models such as those based on the Reynolds-averaged Navier …
calibrate simple engineering models such as those based on the Reynolds-averaged Navier …
Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
There exists significant demand for improved Reynolds-averaged Navier–Stokes (RANS)
turbulence models that are informed by and can represent a richer set of turbulence physics …
turbulence models that are informed by and can represent a richer set of turbulence physics …
Physics-informed machine learning approach for augmenting turbulence models: A comprehensive framework
Reynolds-averaged Navier-Stokes (RANS) equations are widely used in engineering
turbulent flow simulations. However, RANS predictions may have large discrepancies due to …
turbulent flow simulations. However, RANS predictions may have large discrepancies due to …
Data‐driven equation discovery of ocean mesoscale closures
The resolution of climate models is limited by computational cost. Therefore, we must rely on
parameterizations to represent processes occurring below the scale resolved by the models …
parameterizations to represent processes occurring below the scale resolved by the models …
Applications of deep learning to ocean data inference and subgrid parameterization
Oceanographic observations are limited by sampling rates, while ocean models are limited
by finite resolution and high viscosity and diffusion coefficients. Therefore, both data from …
by finite resolution and high viscosity and diffusion coefficients. Therefore, both data from …
Applying machine learning to study fluid mechanics
SL Brunton - Acta Mechanica Sinica, 2021 - Springer
This paper provides a short overview of how to use machine learning to build data-driven
models in fluid mechanics. The process of machine learning is broken down into five …
models in fluid mechanics. The process of machine learning is broken down into five …
[PDF][PDF] CFD 模拟方法的发展成就与展望
阎超， 于剑， 徐晶磊， 范晶晶， 高瑞泽， 姜振华 - 力学进展, 2011 - lxjz.cstam.org.cn
CFD 模拟方法的发展成就与展望 Page 1 第41 卷第5 期 力学进展 Vol. 41 No. 5 2011 年9 月25
日 ADVANCES IN MECHANICS Sep. 25, 2011 CFD 模拟方法的发展成就与展望 ∗ 阎超† 于剑 …
日 ADVANCES IN MECHANICS Sep. 25, 2011 CFD 模拟方法的发展成就与展望 ∗ 阎超† 于剑 …
An interpretable framework of data-driven turbulence modeling using deep neural networks
Reynolds-averaged Navier–Stokes simulations represent a cost-effective option for practical
engineering applications, but are facing ever-growing demands for more accurate …
engineering applications, but are facing ever-growing demands for more accurate …
Prediction model of velocity field around circular cylinder over various Reynolds numbers by fusion convolutional neural networks based on pressure on the cylinder
A data-driven model is proposed for the prediction of the velocity field around a cylinder by
fusion convolutional neural networks (CNNs) using measurements of the pressure field on …
fusion convolutional neural networks (CNNs) using measurements of the pressure field on …