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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…