Surrogate models for groundwater flow simulations

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11 to 20 of 14,252 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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