7 citations · 14 across the 3 of their papers we have counts for
9 papers
Obtaining the mean fields with known Reynolds stresses at steady state
Xianwen Guo, Zhenhua Xia, Heng Xiao +2
With the rising of modern data science, data--driven turbulence modeling with the aid of machine learning algorithms is becoming a new promising field. Many approaches are able to…
Enforcing Deterministic Constraints on Generative Adversarial Networks for Emulating Physical Systems
Zeng Yang, Jin-Long Wu, Heng Xiao
Generative adversarial networks (GANs) were initially proposed to generate images by learning from a large number of samples. Recently, GANs have been used to emulate complex physi…
Flows Over Periodic Hills of Parameterized Geometries: A Dataset for Data-Driven Turbulence Modeling From Direct Simulations
Heng Xiao, Jin-Long Wu, Sylvain Laizet +1
Computational fluid dynamics models based on Reynolds-averaged Navier--Stokes equations with turbulence closures still play important roles in engineering design and analysis. Howe…
Enforcing Statistical Constraints in Generative Adversarial Networks for Modeling Chaotic Dynamical Systems
Jin-Long Wu, Karthik Kashinath, Adrian Albert +3
Simulating complex physical systems often involves solving partial differential equations (PDEs) with some closures due to the presence of multi-scale physics that cannot be fully…
Seeing Permeability From Images: Fast Prediction with Convolutional Neural Networks
Jin-Long Wu, Xiao-Long Yin, Heng Xiao
Fast prediction of permeability directly from images enabled by image recognition neural networks is a novel pore-scale modeling method that has a great potential. This article pre…
RANS Equations with Explicit Data-Driven Reynolds Stress Closure Can Be Ill-Conditioned
Jin-Long Wu, Heng Xiao, Rui Sun +1
Reynolds-averaged Navier--Stokes (RANS) simulations with turbulence closure models continue to play important roles in industrial flow simulations. However, the commonly used linea…