Supplemental data for "Accelerating Conjugate Gradient Solvers for Homogenization Problems with Unitary Neural Operators"

All results and figures in the article can be reproduced using the mentioned software package together with the data sets available in this DaRUS repository. As part of the training data and evaluation data, 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].

Further information is available in the `README.md` file of this repository. [1] Herb, J. and Fritzen, F. (2026), Accelerating Conjugate Gradient Solvers for Homogenization Problems with Unitary Neural Operators. Int J Numer

Methods

Eng. doi.org/10.1002/nme.70277 [2] Lißner, J. (2020). 2d microstructure data (Version V2) [dataset]. DaRUS. doi.org/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. doi.org/10.5281/zenodo.4047774