DaRUS

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This is the Data Repository of the University of Stuttgart.

SFB 1313 "Interface-Driven Multi-Field Processes in Porous Media"

SFB 1333 "Molecular heterogeneous catalysis in confined geometries"

Institute of Biochemistry

Institute of Applied Mechanics (MIB)

Institute of Thermodynamics and Thermal Process Engineering

Stuttgart Center for Simulation Science (SC SimTech)

Cluster of Excellence Integrative Computational Design and Construction for Architecture (EXC IntCDC)

SFB/Transregio 161 "Quantitative Methods for Visual Computing"

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11 to 20 of 126,753 Results

UNet_randomK101dp_step1.zip Jul 4, 2025 - Trained Vanilla Models on Synthetic Permeability Fields, 101 Data Points ZIP Archive - 70.7 MB - MD5: b3c6bbdc7fdefa13268641dd3c31321f
DDUNet_randomK101dp_step3.zip Jul 4, 2025 - Trained Vanilla Models on Synthetic Permeability Fields, 101 Data Points ZIP Archive - 113.9 MB - MD5: 392c634768bc3011df9aef36efe756d8
UNet_randomK101dp_step3.zip Jul 4, 2025 - Trained Vanilla Models on Synthetic Permeability Fields, 101 Data Points ZIP Archive - 49.2 MB - MD5: dad725053198dea17e01f90c2adf3f27
Trained Models on Real Permeability Fields, 4+1 Data Points Jul 4, 2025 - Few-Shot Learning by Explicit Physics Integration: An Application to Groundwater Heat Transport Pelzer, Julia, 2025, "Trained Models on Real Permeability Fields, 4+1 Data Points", https://doi.org/10.18419/DARUS-5082, DaRUS, V1 Models are trained with Heat Plume Prediction on 4 data points (dp). Steps 1 and 3 of LGCNN (Local Global Convolutional Neural Network) are separate, step 2 is a numerical solver that does not require any trained model. For inference follow the guidelines of Heat Plume Prediction and applied all 3 steps/models sequentially to your input data. Based...
LGCNN_step1_realK.zip Jul 4, 2025 - Trained Models on Real Permeability Fields, 4+1 Data Points ZIP Archive - 177.4 MB - MD5: 78ca4f2b6f5e1fcb4643b9102b55573a Data
LGCNN_step3_realK.zip Jul 4, 2025 - Trained Models on Real Permeability Fields, 4+1 Data Points ZIP Archive - 87.6 MB - MD5: b8507c67b68f7082cdc1c53ece100ca0 Data
Trained Models on Synthetic Permeability Fields, 3+1 Data Points Jul 4, 2025 - Few-Shot Learning by Explicit Physics Integration: An Application to Groundwater Heat Transport Pelzer, Julia, 2025, "Trained Models on Synthetic Permeability Fields, 3+1 Data Points", https://doi.org/10.18419/DARUS-5080, DaRUS, V1 Models are trained with Heat Plume Prediction. Steps 1 and 3 of LGCNN (Local Global Convolutional Neural Network) are separate, step 2 is a numerical solver that does not require any trained model. The vanilla UNet can be applied directly end-to-end, just does not give very good results. For inference follow the guidelines of Heat Plume Prediction...
LGCNN_step1_randomK.zip Jul 4, 2025 - Trained Models on Synthetic Permeability Fields, 3+1 Data Points ZIP Archive - 157.6 MB - MD5: 68fd3a2c83e75684972a2e8dc3cac5ec
LGCNN_step3_randomK.zip Jul 4, 2025 - Trained Models on Synthetic Permeability Fields, 3+1 Data Points ZIP Archive - 135.0 MB - MD5: 12b4c1cf242036e444b22762d056a819
vanillaUNet.zip Jul 4, 2025 - Trained Models on Synthetic Permeability Fields, 3+1 Data Points ZIP Archive - 697.8 MB - MD5: 197acd0a0e1ae1cfe78aa0367dfea382

UNet_randomK101dp_step1.zip

Jul 4, 2025 - Trained Vanilla Models on Synthetic Permeability Fields, 101 Data Points

ZIP Archive - 70.7 MB -

DDUNet_randomK101dp_step3.zip

Jul 4, 2025 - Trained Vanilla Models on Synthetic Permeability Fields, 101 Data Points

ZIP Archive - 113.9 MB -

UNet_randomK101dp_step3.zip

Jul 4, 2025 - Trained Vanilla Models on Synthetic Permeability Fields, 101 Data Points

ZIP Archive - 49.2 MB -

Trained Models on Real Permeability Fields, 4+1 Data Points

Jul 4, 2025 - Few-Shot Learning by Explicit Physics Integration: An Application to Groundwater Heat Transport

Pelzer, Julia, 2025, "Trained Models on Real Permeability Fields, 4+1 Data Points", https://doi.org/10.18419/DARUS-5082, DaRUS, V1

Models are trained with Heat Plume Prediction on 4 data points (dp). Steps 1 and 3 of LGCNN (Local Global Convolutional Neural Network) are separate, step 2 is a numerical solver that does not require any trained model. For inference follow the guidelines of Heat Plume Prediction and applied all 3 steps/models sequentially to your input data. Based...

LGCNN_step1_realK.zip

Jul 4, 2025 - Trained Models on Real Permeability Fields, 4+1 Data Points

ZIP Archive - 177.4 MB -

Data

LGCNN_step3_realK.zip

Jul 4, 2025 - Trained Models on Real Permeability Fields, 4+1 Data Points

ZIP Archive - 87.6 MB -

Data

Trained Models on Synthetic Permeability Fields, 3+1 Data Points

Jul 4, 2025 - Few-Shot Learning by Explicit Physics Integration: An Application to Groundwater Heat Transport

Pelzer, Julia, 2025, "Trained Models on Synthetic Permeability Fields, 3+1 Data Points", https://doi.org/10.18419/DARUS-5080, DaRUS, V1

Models are trained with Heat Plume Prediction. Steps 1 and 3 of LGCNN (Local Global Convolutional Neural Network) are separate, step 2 is a numerical solver that does not require any trained model. The vanilla UNet can be applied directly end-to-end, just does not give very good results. For inference follow the guidelines of Heat Plume Prediction...

LGCNN_step1_randomK.zip

Jul 4, 2025 - Trained Models on Synthetic Permeability Fields, 3+1 Data Points

ZIP Archive - 157.6 MB -

LGCNN_step3_randomK.zip

Jul 4, 2025 - Trained Models on Synthetic Permeability Fields, 3+1 Data Points

ZIP Archive - 135.0 MB -

vanillaUNet.zip

Jul 4, 2025 - Trained Models on Synthetic Permeability Fields, 3+1 Data Points

ZIP Archive - 697.8 MB -

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