Supplemental data for "Robust inverse material design with physical guarantees using the Voigt-Reuss Net"

Version 1.0
Keshav, Sanath; Fritzen, Felix; Boddapati, Jagannadh; Daraio, Chiara, 2026, "Supplemental data for "Robust inverse material design with physical guarantees using the Voigt-Reuss Net"", https://doi.org/10.18419/DARUS-5730, DaRUS, V1, UNF:6:byGE329uA/9Q5pf0cmyyVw== [fileUNF]
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Description
This repository contains supplemental data for the article

"Robust inverse material design with physical guarantees using the Voigt-Reuss Net",

accepted for publication in the International Journal for Numerical Methods in Engineering by Sanath Keshav and Felix Fritzen.

The data in this DaRUS repository complements the accompanying open-source implementation of the Voigt-Reuss net and enables reproducibility of the numerical results in the manuscript [1]. The datasets are generated by solving periodic small-strain linear elasticity homogenization problems for a large number of biphasic periodic RVEs. Effective stiffness tensors were computed with our open-source implementation of the Fourier-Accelerated Nodal Solvers (FANS) [4] on voxelized microstructures under periodic boundary conditions.

The repository contains two labeled datasets:

(i) 3D linear elasticity.
We use an open 3D microstructure dataset (90,000 stochastic microstructures, resolution 192x192x192), together with 236 scalar, image-derived morphological descriptors per microstructure [2]. For each microstructure, we sample three non-dimensional parameters that encode the bulk and shear moduli of the two phases, and compute the corresponding effective stiffness tensor (symmetric positive definite, 6x6 in Mandel notation). Overall, the dataset contains ~1.18 million microstructure-material combinations and includes the train/validation/test splits used in the paper.

(ii) 2D plane-strain elasticity.
We use periodic microstructures obtained from thresholded trigonometric fields parameterized by an amplitude matrix A and a threshold [3]. For each sample, we provide the generator parameters, the rendered microstructure image, and the homogenized plane-strain stiffness tensor (symmetric positive definite, 3x3 in Voigt notation). The same split definitions and metadata used for training and evaluation are included to reproduce the forward-prediction comparisons and inverse-design experiments.

Further details on file formats, naming conventions, and the exact contents of each file are provided in README.md.

[1] Keshav, S., and Fritzen, F. (2026). Robust inverse material design with physical guarantees using the Voigt-Reuss Net, International Journal for Numerical Methods in Engineering. https://doi.org/10.1002/nme.70296

[2] 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

[3] Boddapati, J., & Daraio, C. (2024). Planar structured materials with extreme elastic anisotropy. Materials & Design, 246, 113348. https://doi.org/10.1016/j.matdes.2024.113348

[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

(2026-02-18)
Subject Computer and Information Science; Engineering
Keyword Constitutive Modeling, Homogenization, Composite Material, Multiscale Modeling, Structure-Property Linkage, Voigt-Reuss Net, Inverse Material Design
Related Publication Is Supplement To: S.Keshav, F. Fritzen, Robust inverse material design with physical guarantees using the Voigt-Reuss Net, International Journal for Numerical Methods in Engineering (2026) - in productiondoi: 10.1002/nme.70296
Notes The related publication was accepted for publication on 2026-02-16. The DOI is already assigned and the article will be available "open access" once published.
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feature_engineering_mechanical_2D.h5
Hierarchical Data Format - 150.1 MB
Published Feb 25, 2026
19 Downloads
MD5: 20a6c4396ce6885d78df0ac4e8a2378e
2D mechanical dataset
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feature_engineering_mechanical_3D.h5
Hierarchical Data Format - 34.0 GB
Published Feb 25, 2026
5 Downloads
MD5: f0d2a4500e8a8615edb036d1b3912002
3D mechanical dataset
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metadata_mechanical_3D.tab
Tabular Data - 200.5 MB
Published Feb 25, 2026
2,206 Downloads
8 Variables, 1185030 Observations UNF:6:byGE329uA/9Q5pf0cmyyVw==
3D mechanical dataset (Metadata)
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README.md
Markdown Text - 10.8 KB
Published Feb 25, 2026
3 Downloads
MD5: 2b6b7ea06416094a0ba3b0b63e6c1b3a
Supplementary information
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Curation Status History
StatusDateAssigner
2026-02-25 13:06:25Felix Fritzen
In Review by FoKUS2026-02-25 12:48:59Dorothea Iglezakis
Waiting for Feedback from Author2026-02-25 09:56:58Dorothea Iglezakis
In Review by FoKUS2026-02-24 10:04:33Dorothea Iglezakis
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