ML Session: Vector-Cloud Neural Networks for Nonlocal Constitutive Modeling (Heng Xiao)

We are happy to announce the next presentation in the ML-Session series:

Heng Xiao (University of Stuttgart) will present on Wednesday 8 November 2023 at 2pm in PWR 57, 8.122 a in person lecture on "Vector-Cloud Neural Networks for Nonlocal Constitutive Modeling"

Abstract: Developing robust constitutive (or closure) 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 from unstructured data. The model predicts the closure variable at a point based on a collection of neighboring points (referred to as a “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.