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Metadata Source: DaRUS

361 to 370 of 127,101 Results

DDUNet_randomK101dp_full.zip Jul 4, 2025 - Trained Vanilla Models on Synthetic Permeability Fields, 101 Data Points ZIP Archive - 249.9 MB - MD5: 520c764c272ee8dd5f6696b7187cdd36
UNet_randomK101dp_full.zip Jul 4, 2025 - Trained Vanilla Models on Synthetic Permeability Fields, 101 Data Points ZIP Archive - 89.5 MB - MD5: 9c86832a36f3b724b630a0dcef078b72
DDUNet_randomK101dp_step1.zip Jul 4, 2025 - Trained Vanilla Models on Synthetic Permeability Fields, 101 Data Points ZIP Archive - 77.1 MB - MD5: 16bfb4fc226797cbba87899632b865bb
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...

DDUNet_randomK101dp_full.zip

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

ZIP Archive - 249.9 MB -

UNet_randomK101dp_full.zip

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

ZIP Archive - 89.5 MB -

DDUNet_randomK101dp_step1.zip

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

ZIP Archive - 77.1 MB -

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...

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