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Deep learning for fluid velocity field estimation: A review
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 …
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 …
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 …
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 …
Deep recurrent optical flow learning for particle image velocimetry data
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 develo** 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 …
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 …
When deep learning meets digital image correlation
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 …