NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations
In the last 50 years there has been a tremendous progress in solving numerically the Navier-
Stokes equations using finite differences, finite elements, spectral, and even meshless …
Stokes equations using finite differences, finite elements, spectral, and even meshless …
Flow over an espresso cup: inferring 3-D velocity and pressure fields from tomographic background oriented Schlieren via physics-informed neural networks
Tomographic background oriented Schlieren (Tomo-BOS) imaging measures density or
temperature fields in three dimensions using multiple camera BOS projections, and is …
temperature fields in three dimensions using multiple camera BOS projections, and is …
Review of machine learning for hydrodynamics, transport, and reactions in multiphase flows and reactors
Artificial intelligence (AI), machine learning (ML), and data science are leading to a
promising transformative paradigm. ML, especially deep learning and physics-informed ML …
promising transformative paradigm. ML, especially deep learning and physics-informed ML …
Machine learning approach for flow fields over a circular cylinder based on particle image velocimetry measurements
Flow around a circular cylinder has been experimentally studied at Reynolds numbers of
Re= 4× 10 3 and Re= 8× 10 3 using Particle Image Velocimetry (PIV). The Artificial Neural …
Re= 4× 10 3 and Re= 8× 10 3 using Particle Image Velocimetry (PIV). The Artificial Neural …
物理增强的流场深度学习建模与模拟方法
金晓威， 李惠 - 力学学报, 2021 - lxxb.cstam.org.cn
流体运动理论上可用Navier− Stokes 方程描述, 但由于对流项带来的非线性,
仅在少数情况可求得方程解析解. 对于复杂工程流动问题, 数值模拟难以高效精准计算高雷诺数 …
仅在少数情况可求得方程解析解. 对于复杂工程流动问题, 数值模拟难以高效精准计算高雷诺数 …
DeepTRNet: Time-resolved reconstruction of flow around a circular cylinder via spatiotemporal deep neural networks
We propose spatiotemporal deep neural networks for the time-resolved reconstruction of the
velocity field around a circular cylinder (DeepTRNet) based only on two flow data types: the …
velocity field around a circular cylinder (DeepTRNet) based only on two flow data types: the …
Towards real-time prediction of velocity field around a building using generative adversarial networks based on the surface pressure from sparse sensor networks
In this study, we used machine learning techniques to predict instantaneous velocity fields
around a single building, where a limited amount of surface pressure data obtained from …
around a single building, where a limited amount of surface pressure data obtained from …
Machine learning for flow field measurements: a perspective
S Discetti, Y Liu - Measurement Science and Technology, 2022 - iopscience.iop.org
Advancements in machine-learning (ML) techniques are driving a paradigm shift in image
processing. Flow diagnostics with optical techniques is not an exception. Considering the …
processing. Flow diagnostics with optical techniques is not an exception. Considering the …
Predictions of transient vector solution fields with sequential deep operator network
The deep operator network (DeepONet) structure has shown great potential in
approximating complex solution operators with low generalization errors. Recently, a …
approximating complex solution operators with low generalization errors. Recently, a …
Physics-enhanced deep learning methods for modelling and simulating flow fields
Fluid flows can be theoretically described by the Navier− Stokes equations. However, due to
the nonlinear convection term, analytical solutions of the equations can only be obtained for …
the nonlinear convection term, analytical solutions of the equations can only be obtained for …