Deep learning for fluid velocity field estimation: A review
C Yu, X Bi, Y Fan - Ocean Engineering, 2023 - Elsevier
Deep learning technique, has made tremendous progress in fluid mechanics in recent
years, because of its mighty feature extraction capacity from complicated and massive fluid …
years, because of its mighty feature extraction capacity from complicated and massive fluid …
Deep learning for digital holography: a review
Recent years have witnessed the unprecedented progress of deep learning applications in
digital holography (DH). Nevertheless, there remain huge potentials in how deep learning …
digital holography (DH). Nevertheless, there remain huge potentials in how deep learning …
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 …
Unsupervised deep learning for super-resolution reconstruction of turbulence
Recent attempts to use deep learning for super-resolution reconstruction of turbulent flows
have used supervised learning, which requires paired data for training. This limitation …
have used supervised learning, which requires paired data for training. This limitation …
Uncertainty quantification for noisy inputs–outputs in physics-informed neural networks and neural operators
Uncertainty quantification (UQ) in scientific machine learning (SciML) becomes increasingly
critical as neural networks (NNs) are being widely adopted in addressing complex problems …
critical as neural networks (NNs) are being widely adopted in addressing complex problems …
Deep recurrent optical flow learning for particle image velocimetry data
C Lagemann, K Lagemann, S Mukherjee… - Nature Machine …, 2021 - nature.com
A wide range of problems in applied physics and engineering involve learning physical
displacement fields from data. In this paper we propose a deep neural network-based …
displacement fields from data. In this paper we propose a deep neural network-based …
Artificial intelligence velocimetry and microaneurysm-on-a-chip for three-dimensional analysis of blood flow in physiology and disease
Understanding the mechanics of blood flow is necessary for developing insights into
mechanisms of physiology and vascular diseases in microcirculation. Given the limitations of …
mechanisms of physiology and vascular diseases in microcirculation. Given the limitations of …
When deep learning meets digital image correlation
S Boukhtache, K Abdelouahab, F Berry… - Optics and Lasers in …, 2021 - Elsevier
Abstract Convolutional Neural Networks (CNNs) constitute a class of Deep Learning models
which have been used in the recent past to resolve many problems in computer vision, in …
which have been used in the recent past to resolve many problems in computer vision, in …
Reconstructing turbulent velocity and pressure fields from under-resolved noisy particle tracks using physics-informed neural networks
Volume-resolving imaging techniques are rapidly advancing progress in experimental fluid
mechanics. However, reconstructing the full and structured Eulerian velocity and pressure …
mechanics. However, reconstructing the full and structured Eulerian velocity and pressure …