An equivariant neural operator for developing nonlocal tensorial constitutive models

TL;DR

This paper introduces a neural operator, VCNN-e, that respects physical invariances and can develop nonlocal tensorial constitutive models, demonstrated by emulating Reynolds stress models for turbulent flows.

Contribution

The paper presents a novel equivariant neural operator that models nonlocal tensorial quantities while maintaining physical invariance properties, applicable across different spatial resolutions.

Findings

VCNN-e accurately emulates Reynolds stress models.

The model respects invariance and equivariance properties.

It demonstrates potential for robust turbulence modeling.

Abstract

Developing robust constitutive models is a fundamental and longstanding problem for accelerating the simulation of complicated physics. Machine learning provides promising tools to construct constitutive models based on various calibration data. In this work, we propose a neural operator to develop nonlocal constitutive models for tensorial quantities through a vector-cloud neural network with equivariance (VCNN-e). The VCNN-e respects all the invariance properties desired by constitutive models, faithfully reflects the region of influence in physics, and is applicable to different spatial resolutions. By design, the model guarantees that the predicted tensor is invariant to the frame translation and ordering (permutation) of the neighboring points. Furthermore, it is equivariant to the frame rotation, i.e., the output tensor co-rotates with the coordinate frame. We evaluate the VCNN-e by using it to emulate the Reynolds stress transport model for turbulent flows, which directly computes the Reynolds stress tensor to close the Reynolds-averaged Navier--Stokes (RANS) equations. The evaluation is performed in two situations: (1) emulating the Reynolds stress model through synthetic data generated from the Reynolds stress transport equations with closure models, and (2) predicting the Reynolds stress by learning from data generated from direct numerical simulations. Such a priori evaluations of the proposed network pave the way for developing and calibrating robust and nonlocal, non-equilibrium closure models for the RANS equations.