Multi-scale reconstruction of turbulent rotating flows with proper orthogonal decomposition and generative adversarial networks
Data reconstruction of rotating turbulent snapshots is investigated utilizing data-driven tools.
This problem is crucial for numerous geophysical applications and fundamental aspects …
This problem is crucial for numerous geophysical applications and fundamental aspects …
Three-dimensional generative adversarial networks for turbulent flow estimation from wall measurements
Different types of neural networks have been used to solve the flow sensing problem in
turbulent flows, namely to estimate velocity in wall-parallel planes from wall measurements …
turbulent flows, namely to estimate velocity in wall-parallel planes from wall measurements …
Multi-scale reconstruction of turbulent rotating flows with generative diffusion models
We address the problem of data augmentation in a rotating turbulence set-up, a
paradigmatic challenge in geophysical applications. The goal is to reconstruct information in …
paradigmatic challenge in geophysical applications. The goal is to reconstruct information in …
Stochastic Reconstruction of Gappy Lagrangian Turbulent Signals by Conditional Diffusion Models
We present a stochastic method for reconstructing missing spatial and velocity data along
the trajectories of small objects passively advected by turbulent flows with a wide range of …
the trajectories of small objects passively advected by turbulent flows with a wide range of …
Turbulence scaling from deep learning diffusion generative models
Complex spatial and temporal structures are inherent characteristics of turbulent fluid flows
and comprehending them poses a major challenge. This comprehension necessitates an …
and comprehending them poses a major challenge. This comprehension necessitates an …
From Sparse to Dense Representations in Open Channel Flow Images with Convolutional Neural Networks
Convolutional neural networks (CNN) have been widely adopted in fluid dynamics
investigations over the past few years due to their ability to extract and process fluid flow …
investigations over the past few years due to their ability to extract and process fluid flow …
Some effects of limited wall-sensor availability on flow estimation with 3D-GANs
A Cuéllar, A Ianiro, S Discetti - Theoretical and Computational Fluid …, 2024 - Springer
In this work we assess the impact of the limited availability of wall-embedded sensors on the
full 3D estimation of the flow field in a turbulent channel with R e τ= 200. The estimation …
full 3D estimation of the flow field in a turbulent channel with R e τ= 200. The estimation …
Generative diffusion models for synthetic trajectories of heavy and light particles in turbulence
Heavy and light particles are commonly found in many natural phenomena and industrial
processes, such as suspensions of bubbles, dust, and droplets in incompressible turbulent …
processes, such as suspensions of bubbles, dust, and droplets in incompressible turbulent …
A multiscale and multicriteria Generative Adversarial Network to synthesize 1-dimensional turbulent fields
CG Belinchon, MC Gallucci - Machine Learning: Science and …, 2024 - iopscience.iop.org
This article introduces a new neural network stochastic model to generate a 1-dimensional
stochastic field with turbulent velocity statistics. Both the model architecture and training …
stochastic field with turbulent velocity statistics. Both the model architecture and training …
The impact of AI on engineering design procedures for dynamical systems
KM de Payrebrune, K Flaßkamp, T Ströhla… - arXiv preprint arXiv …, 2024 - arxiv.org
Artificial intelligence (AI) is driving transformative changes across numerous fields,
revolutionizing conventional processes and creating new opportunities for innovation. The …
revolutionizing conventional processes and creating new opportunities for innovation. The …