Strain‐Space Hyperreduction Using Empirically Corrected Cluster Cubature With Linear and Quadratic Approximation Spaces

ABSTRACT Strain‐space reduced cubature techniques such as the Empirically Corrected Cluster Cubature (E3C) considerably reduce the integration effort incurred in computational homogenisation problems. In this work, we apply E3C in tandem with projection‐based model reduction techniques utilising both linear and quadratic approximation spaces. For simulations on an example porous hyperelastic representative volume element (RVE), E3C leads to considerably improved performance, whether with linear or quadratic approximation spaces, over alternative hyperreduction approaches explored in our previous work. As expected, a quadratic ansatz allows for the construction of smaller approximation spaces than a linear one, with equal accuracy. However, in the example explored here, this does not translate into an improved tradeoff between accuracy and runtime.

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Journal
PAMM
Published
2026-09-21
DOI
https://doi.org/10.1002/pamm.70213
Primary Topic
Advanced Mathematical Modeling in Engineering
Type
article
Field-Weighted Citation Impact
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article

Strain‐Space Hyperreduction Using Empirically Corrected Cluster Cubature With Linear and Quadratic Approximation Spaces

Erik Faust, Dong Zhao, Lisa Scheunemann
PAMM
Advanced Mathematical Modeling in Engineering
article

Strain‐Space Hyperreduction Using Empirically Corrected Cluster Cubature With Linear and Quadratic Approximation Spaces

Erik Faust, Dong Zhao, Lisa Scheunemann
article en

Abstract

ABSTRACT Strain‐space reduced cubature techniques such as the Empirically Corrected Cluster Cubature (E3C) considerably reduce the integration effort incurred in computational homogenisation problems. In this work, we apply E3C in tandem with projection‐based model reduction techniques utilising both linear and quadratic approximation spaces. For simulations on an example porous hyperelastic representative volume element (RVE), E3C leads to considerably improved performance, whether with linear or quadratic approximation spaces, over alternative hyperreduction approaches explored in our previous work. As expected, a quadratic ansatz allows for the construction of smaller approximation spaces than a linear one, with equal accuracy. However, in the example explored here, this does not translate into an improved tradeoff between accuracy and runtime.

PAMMVol. 26(4)
University of Kaiserslautern (DE), University of Applied Sciences Kaiserslautern (DE), RWTH Aachen University (DE)
Openalex Percentile: Top 9%
Advanced Mathematical Modeling in Engineering
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Strain‐Space Hyperreduction Using Empirically Corrected Cluster Cubature With Linear and Quadratic Approximation Spaces — Erik Faust, Dong Zhao, et al. · PAMM (2026) | TGRS Research Map | TGRS