Supplemental data for "Spectral Normalization and Voigt–Reuss net: A universal approach to microstructure‐property forecasting with physical guarantees"

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Keshav, Sanath; Herb, Julius; Fritzen, Felix, 2025, "Supplemental data for "Spectral Normalization and Voigt–Reuss net: A universal approach to microstructure‐property forecasting with physical guarantees"", https://doi.org/10.18419/DARUS-5120, DaRUS, V1
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This repository contains supplemental data for the article

"Spectral Normalization and Voigt-Reuss net: A universal approach to microstructure‐property forecasting with physical guarantees",

accepted for publication in GAMM-Mitteilungen by Sanath Keshav, Julius Herb, and Felix Fritzen [1]. The data contained in this DaRUS repository acts as an extension to the GitHub repository for the so-called Voigt-Reuss net. The data in this dataset is generated by solving thermal homogenization problems for an abundance of different microstructures. The microstructures are defined by periodic representative volume elements (RVE) and periodic boundary conditions are applied to the temperature fluctuations.
We consider bi-phasic two-dimensional microstructures with a resolution of 400 × 400 pixels, as published in [2], and three-dimensional microstructures with a resolution of 192 × 192 × 192 voxels, as published in [3]. For both microstructure datasets, we provide the effective thermal conductivity tensor that is obtained by solving homogenization problems on the full microstructure for different material parameters in the two phases. For the simulation, we used our implementation of Fourier-Accelerated Nodal Solvers (FANS, [4]) that is based on a Finite Element Method (FEM) discretization. Further details are provided in the README.md file of this dataset, in our manuscript [1], and in the GitHub repository.

[1] Keshav, S., Herb, J., and Fritzen, F. (2025). Spectral Normalization and Voigt–Reuss net: A universal approach to microstructure‐property forecasting with physical guarantees, GAMM‐Mitteilungen. (2025), e70005. https://doi.org/10.1002/gamm.70005

[2] Lißner, J. (2020). 2d microstructure data (Version V2) [dataset]. DaRUS. https://doi.org/doi:10.18419/DARUS-1151

[3] Prifling, B., Röding, M., Townsend, P., Neumann, M., and Schmidt, V. (2020). Large-scale statistical learning for mass transport prediction in porous materials using 90,000 artificially generated microstructures [dataset]. Zenodo. https://doi.org/10.5281/zenodo.4047774

[4] Leuschner, M., and Fritzen, F. (2018). Fourier-Accelerated Nodal Solvers (FANS) for homogenization problems. Computational Mechanics, 62(3), 359-392. https://doi.org/10.1007/s00466-017-1501-5

(2025-06-23)
Subject Computer and Information Science; Engineering
Keyword Constitutive Modeling, Homogenization, Composite Material, Multiscale Modeling, Microstructure Homogenization, Structure-Property Linkage, Voigt-Reuss Net
Related Publication Is Supplement To: S. Keshav, J. Herb and F. Fritzen, Spectral Normalization and Voigt–Reuss net: A universal approach to microstructure‐property forecasting with physical guarantees, GAMM‐Mitteilungen. (2025), e70005.doi: 10.1002/gamm.70005
Notes Related publication was accepted for publication on 2025-06-13.
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