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May 22, 2026 - Surrogate models for groundwater flow simulations
Baratto, Thomas, 2026, "Trained Neural Networks on Simulated Data of Groundwater Heat Plume Characteristics", https://doi.org/10.18419/DARUS-5815, DaRUS, V3, UNF:6:dsogzGIJc3jbogD4D0lTEA== [fileUNF]
Inference package for thermal plume prediction (v1.0.0). Contains pre-trained MLP and randomized neural network models, the ba-predict CLI, the CSV data used to train the models obtained via simulation by Fabian Böttcher, sample input files, and a Dockerfile. CPU-only (no GPU required). Install with: pip install ./code. Full source code (training s... |
Tabular Data - 7.7 MB - 13 Variables, 85531 Observations - UNF:6:6k+73yoVIjlpTOs2PFnb6w==
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Python Source Code - 3.5 KB -
MD5: 368dbaca48a93951005c27c5fe31a22b
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Python Source Code - 3.9 KB -
MD5: 80441dc0119d4a835dd421e304b68427
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Python Source Code - 3.9 KB -
MD5: 80441dc0119d4a835dd421e304b68427
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Tabular Data - 436.7 KB - 5 Variables, 12835 Observations - UNF:6:/zEueaz1Rf9v1RaAambp4g==
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Docker Image File - 992 B -
MD5: 8b4f525f16574fe8aec70d60186605fc
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Python Source Code - 5.0 KB -
MD5: 8949a6d8d6c56a5b0deffab75261fc6f
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Python Source Code - 5.0 KB -
MD5: 8949a6d8d6c56a5b0deffab75261fc6f
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Python Source Code - 2.9 KB -
MD5: 413ef4a8b8cde57fe51d3b388b79b556
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