English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In the recent application of scientific modeling, machine learning models arelargely applied to facilitate computational simulations of fluid systems.Rotation symmetry is a general property for most symmetric fluid systems.However, in general, current machine learning methods have no theoretical wayto guarantee rotational symmetry. By observing an important property ofcontraction and rotation operation on high-order symmetric tensors, we provethat the rotation operation is preserved via tensor contraction. Based on thistheoretical justification, in this paper, we introduce Rotation-EquivariantNetwork (RotEqNet) to guarantee the property of rotation-equivariance forhigh-order tensors in fluid systems. We implement RotEqNet and evaluate ourclaims through four case studies on various fluid systems. The property oferror reduction and rotation-equivariance is verified in these case studies.Results from the comparative study show that our method outperformsconventional methods, which rely on data augmentation.

RotEqNet: Rotation-Equivariant Network for Fluid Systems with Symmetric High-Order Tensors