Trained Models on Synthetic Permeability Fields, 3+1 Data Points
收藏DataCite Commons2025-07-04 更新2026-05-07 收录
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https://darus.uni-stuttgart.de/citation?persistentId=doi:10.18419/DARUS-5080
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资源简介：
Models are trained with <a href="https://github.com/JuliaPelzer/Heat-Plume-Prediction/tree/AllIn1/LGCNN/release25">Heat Plume Prediction</a>.
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 <a href="https://github.com/JuliaPelzer/Heat-Plume-Prediction/tree/AllIn1/LGCNN/release25">Heat Plume Prediction</a> and applied all 3 steps/models sequentially to your input data.
Based on raw data from <a href="https://doi.org/10.18419/darus-5063">https://doi.org/10.18419/darus-5063</a>.
提供机构：
DaRUS
创建时间：
2025-05-21
