1.5k citations · 5.1k across the 48 of their papers we have counts for
49 papers
Physical interpretation of neural network-based nonlinear eddy viscosity models
Xin-Lei Zhang, Heng Xiao, Solkeun Jee +1
Neural network-based turbulence modeling has gained significant success in improving turbulence predictions by incorporating high--fidelity data. However, the interpretability of t…
Combining direct and indirect sparse data for learning generalizable turbulence models
Xin-Lei Zhang, Heng Xiao, Xiaodong Luo +1
Learning turbulence models from observation data is of significant interest in discovering a unified model for a broad range of practical flow applications. Either the direct obser…
A PDE-free, neural network-based eddy viscosity model coupled with RANS equations
Ruiying Xu, Xu-Hui Zhou, Jiequn Han +2
Most turbulence models used in Reynolds-averaged Navier-Stokes (RANS) simulations are partial differential equations (PDE) that describe the transport of turbulent quantities. Such…
An equivariant neural operator for developing nonlocal tensorial constitutive models
Jiequn Han, Xu-Hui Zhou, Heng Xiao
Developing robust constitutive models is a fundamental and longstanding problem for accelerating the simulation of complicated physics. Machine learning provides promising tools to…
Frame invariance and scalability of neural operators for partial differential equations
Muhammad I. Zafar, Jiequn Han, Xu-Hui Zhou +1
Partial differential equations (PDEs) play a dominant role in the mathematical modeling of many complex dynamical processes. Solving these PDEs often requires prohibitively high co…
Neural Network Based Pore Flow Field Prediction in Porous Media Using Super Resolution
Xu-Hui Zhou, James E. McClure, Cheng Chen +1
Direct pore-scale simulations of fluid flow through porous media are computationally expensive to perform for realistic systems. Previous works have demonstrated using the geometry…
Ensemble gradient for learning turbulence models from indirect observations
Carlos A. Michelén Ströfer, Xin-Lei Zhang, Heng Xiao
Training data-driven turbulence models with high fidelity Reynolds stress can be impractical and recently such models have been trained with velocity and pressure measurements. For…
End-to-end differentiable learning of turbulence models from indirect observations
Carlos A. Michelén Ströfer, Heng Xiao
The emerging push of the differentiable programming paradigm in scientific computing is conducive to training deep learning turbulence models using indirect observations. This pape…
Assimilation of disparate data for enhanced reconstruction of turbulent mean flows
Xin-Lei Zhang, Heng Xiao, Guo-Wei He +1
Reconstruction of turbulent flow based on data assimilation methods is of significant importance for improving the estimation of flow characteristics by incorporating limited obser…
Recurrent Neural Network for End-to-End Modeling of Laminar-Turbulent Transition
Muhammad I. Zafar, Meelan M. Choudhari, Pedro Paredes +1
Accurate prediction of laminar-turbulent transition is a critical element of computational fluid dynamics simulations for aerodynamic design across multiple flow regimes. Tradition…
Frame-independent vector-cloud neural network for nonlocal constitutive modeling on arbitrary grids
Xu-Hui Zhou, Jiequn Han, Heng Xiao
Constitutive models are widely used for modeling complex systems in science and engineering, where first-principle-based, well-resolved simulations are often prohibitively expensiv…
DAFI: An Open-Source Framework for Ensemble-Based Data Assimilation and Field Inversion
Carlos A. Michelén Ströfer, Xin-Lei Zhang, Heng Xiao
In many areas of science and engineering, it is a common task to infer physical fields from sparse observations. This paper presents the DAFI code intended as a flexible framework…
Learning nonlocal constitutive models with neural networks
Xu-Hui Zhou, Jiequn Han, Heng Xiao
Constitutive and closure models play important roles in computational mechanics and computational physics in general. Classical constitutive models for solid and fluid materials ar…
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…
Convolutional Neural Network for Transition Modeling Based on Linear Stability Theory
Muhammad I. Zafar, Heng Xiao, Meelan M. Choudhari +4
Transition prediction is an important aspect of aerodynamic design because of its impact on skin friction and potential coupling with flow separation characteristics. Traditionally…
Evaluation of ensemble methods for quantifying uncertainties in steady-state CFD applications with small ensemble sizes
Xinlei Zhang, Heng Xiao, Thomas Gomez +1
Bayesian uncertainty quantification (UQ) is of interest to industry and academia as it provides a framework for quantifying and reducing the uncertainty in computational models by…
Enforcing Boundary Conditions on Physical Fields in Bayesian Inversion
Carlos A. Michelén Ströfer, Xinlei Zhang, Heng Xiao +1
Inverse problems in computational mechanics consist of inferring physical fields that are latent in the model describing some observable fields. For instance, an inverse problem of…
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…
Regularized Ensemble Kalman Methods for Inverse Problems
Xin-Lei Zhang, Carlos Michelén-Ströfer, Heng Xiao
Inverse problems are common and important in many applications in computational physics but are inherently ill-posed with many possible model parameters resulting in satisfactory r…
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…
Prediction of Reynolds Stresses in High-Mach-Number Turbulent Boundary Layers using Physics-Informed Machine Learning
Jian-Xun Wang, Junji Huang, Lian Duan +1
Modeled Reynolds stress is a major source of model-form uncertainties in Reynolds-averaged Navier-Stokes (RANS) simulations. Recently, a physics-informed machine-learning (PIML) ap…
Quantification of Model Uncertainty in RANS Simulations: A Review
Heng Xiao, Paola Cinnella
In computational fluid dynamics simulations of industrial flows, models based on the Reynolds-averaged Navier--Stokes (RANS) equations are expected to play an important role in dec…