Online Seminar Series - Dr. Heng Xiao
Learning nonlocal turbulence models
Learning nonlocal turbulence models with frame-independent vector-cloud
neural network
Dr. Heng Xiao
Associate professor, Department of Aerospace & Ocean Engineering
Virginia Tech
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.