Online Seminar Series - Dr. Heng Xiao
Learning nonlocal turbulence models
Learning nonlocal turbulence models with frame-independent vector-cloud
Dr. Heng Xiao
Associate professor, Department of Aerospace & Ocean Engineering
Abstract: Developing robust constitutive models is a fundamental problem for accelerating the simulation of complicated physics such as turbulent flows. Traditional constitutive models based on partial differential equations (PDEs) often lack robustness and are too rigid to accommodate diverse calibration datasets. We propose a frame-independent, nonlocal constitutive model based on a vector-cloud neural network that can be learned with data. The model predicts the closure variable at a point based on a collection of neighboring points (referred to as the "cloud"). The cloud is mapped to the closure variable through a neural network that is invariant both to coordinate translation and rotation and to the ordering of points in the cloud. The merits of the proposed network are demonstrated for scalar and tensor transport PDEs on a family of parameterized periodic hill geometries. The vector-cloud neural network is a promising tool not only as nonlocal constitutive models and but also as general surrogate models for PDEs on irregular domains.
Bio: Dr. Heng Xiao is an associate professor in the Department of Aerospace & Ocean Engineering at Virginia Tech. He obtained his Ph.D. from Princeton University in 2009 and worked as a postdoctoral researcher at ETH Zurich, Switzerland, before joining Virginia Tech in 2013. His research focus on data-driven turbulence modeling, including data assimilation, machine learning, and uncertainty quantification.