Prediction of turbulent channel flow using Fourier neural operator-based machine-learning strategy
Fast and accurate predictions of turbulent flows are of great importance in the science and
engineering field. In this paper, we investigate the implicit U-Net enhanced Fourier neural …
engineering field. In this paper, we investigate the implicit U-Net enhanced Fourier neural …
Conditional neural field latent diffusion model for generating spatiotemporal turbulence
Eddy-resolving turbulence simulations are essential for understanding and controlling
complex unsteady fluid dynamics, with significant implications for engineering and scientific …
complex unsteady fluid dynamics, with significant implications for engineering and scientific …
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 …
High-fidelity reconstruction of large-area damaged turbulent fields with a physically constrained generative adversarial network
The reconstruction of incomplete information in flow fields is a pervasive challenge in
numerous turbulence-related applications. This paper proposes a framework for the high …
numerous turbulence-related applications. This paper proposes a framework for the high …
[HTML][HTML] Scientific machine learning based reduced-order models for plasma turbulence simulations
This paper investigates non-intrusive Scientific Machine Learning (SciML) Reduced-Order
Models (ROMs) for plasma turbulence simulations. In particular, we focus on Operator …
Models (ROMs) for plasma turbulence simulations. In particular, we focus on Operator …
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 …
[HTML][HTML] Flow field recovery in restricted domains using a generative adversarial network framework
This study aims to reconstruct the complete flow field from spatially restricted domain data by
utilizing an enhanced super-resolution generative adversarial network (ESRGAN) model …
utilizing an enhanced super-resolution generative adversarial network (ESRGAN) model …
[HTML][HTML] A real-time solution method for three-dimensional steady temperature field of transformer windings based on mechanism-embedded cascade network
Y Liu, Q Zhao, G Liu, Y Zou, S Zhang, K Wang… - Case Studies in Thermal …, 2024 - Elsevier
To enhance the computation efficiency and accuracy of three-dimensional steady
temperature field of transformer windings, we propose a new non-invasive Reduced Order …
temperature field of transformer windings, we propose a new non-invasive Reduced Order …
Comparison of Generative Learning Methods for Turbulence Modeling
C Drygala, E Ross, F di Mare, H Gottschalk - arXiv preprint arXiv …, 2024 - arxiv.org
Numerical simulations of turbulent flows present significant challenges in fluid dynamics due
to their complexity and high computational cost. High resolution techniques such as Direct …
to their complexity and high computational cost. High resolution techniques such as Direct …
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 …