On the one hand, this DaRUS repository contains microstructure datasets that are used for training and testing of the proposed machine-learned preconditioners:

• 2d_microstructures.h5 contains bi-phasic two-dimensional microstructures with a resolution of 400 × 400 pixels that are a subset of the dataset published in

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

• 3d_microstructures.h5 contains bi-phasic three-dimensional microstructures with a resolution of 192 × 192 × 192 voxels that are a subset of the dataset published in

  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
  

• a PyTorch data loader for both datasets is available in the software repository: https://github.com/DataAnalyticsEngineering/UNOCG/tree/main/unocg/utils/data.py

On the other hand, this DaRUS repository contains the weights of the proposed machine-learned preconditioners trained for various problem formulations:

• weights for the UNO preconditioner: weights_uno_thermal_2d_per.pt
• weights for the UNO preconditioner (naive training): weights_uno_naive_thermal_2d_per.pt
• example in the software repository: https://github.com/DataAnalyticsEngineering/UNOCG/tree/main/examples/ijnme2026/evaluate_thermal_2d_per.ipynb

• weights for the UNO preconditioner: weights_uno_thermal_3d_per.pt
• example in the software repository: https://github.com/DataAnalyticsEngineering/UNOCG/tree/main/examples/ijnme2026/evaluate_thermal_3d_per.ipynb

• weights for the UNO preconditioner: weights_uno_thermal_3d_dir.pt
• example in the software repository: https://github.com/DataAnalyticsEngineering/UNOCG/tree/main/examples/ijnme2026/evaluate_thermal_3d_dir.ipynb

• weights for the UNO preconditioner: weights_uno_mechanical_2d_per.pt
• example in the software repository: https://github.com/DataAnalyticsEngineering/UNOCG/tree/main/examples/ijnme2026/evaluate_mechanical_2d_per.ipynb

• weights for the UNO preconditioner: weights_uno_mechanical_2d_dir.pt
• example in the software repository: https://github.com/DataAnalyticsEngineering/UNOCG/tree/main/examples/ijnme2026/evaluate_mechanical_2d_dir.ipynb

• weights for the UNO preconditioner: weights_uno_mechanical_2d_mixed.pt
• example in the software repository: https://github.com/DataAnalyticsEngineering/UNOCG/tree/main/examples/ijnme2026/evaluate_mechanical_2d_mixed.ipynb

• weights for the UNO preconditioner: weights_uno_mechanical_3d_per.pt
• example in the software repository: https://github.com/DataAnalyticsEngineering/UNOCG/tree/main/examples/ijnme2026/evaluate_mechanical_3d_per.ipynb

• weights for the UNO preconditioner: weights_uno_mechanical_3d_dir.pt
• example in the software repository: https://github.com/DataAnalyticsEngineering/UNOCG/tree/main/examples/ijnme2026/evaluate_mechanical_3d_dir.ipynb

• weights for the UNO preconditioner: weights_uno_mechanical_3d_mixed.pt
• example in the software repository: https://github.com/DataAnalyticsEngineering/UNOCG/tree/main/examples/ijnme2026/evaluate_mechanical_3d_mixed.ipynb