%0 Generic %1 praditia2022replication %A Praditia, Timothy %A Karlbauer, Matthias %A Otte, Sebastian %A Oladyshkin, Sergey %A Butz, Martin V. %A Nowak, Wolfgang %D 2022 %K %R 10.18419/darus-3249 %T Replication Data for: Learning Groundwater Contaminant Diffusion-Sorption Processes with a Finite Volume Neural Network %X This dataset contains diffusion-sorption data, generated with numerical simulation based on three different sorption isotherms, namely the linear, Freundlich, and Langmuir isotherms. This dataset is used to train, validate, and test all the deep learning models that are used in the publication "Learning Groundwater Contaminant Diffusion-Sorption Processes with a Finite Volume Neural Network". The dataset for each sorption isotherm includes the dissolved and total contaminant concentration data, as well as spatial coordinates and timestamps that correspond to the concentration data.More detailed information is also provided in our Github repository (https://github.com/CognitiveModeling/finn) and our submitted paper to the Water Resources Research journal.

@misc{praditia2022replication, abstract = {This dataset contains diffusion-sorption data, generated with numerical simulation based on three different sorption isotherms, namely the linear, Freundlich, and Langmuir isotherms. This dataset is used to train, validate, and test all the deep learning models that are used in the publication "Learning Groundwater Contaminant Diffusion-Sorption Processes with a Finite Volume Neural Network". The dataset for each sorption isotherm includes the dissolved and total contaminant concentration data, as well as spatial coordinates and timestamps that correspond to the concentration data.More detailed information is also provided in our Github repository (https://github.com/CognitiveModeling/finn) and our submitted paper to the Water Resources Research journal. }, added-at = {2023-08-31T15:02:28.000+0200}, affiliation = {Praditia, Timothy/Universität Stuttgart, Karlbauer, Matthias/Universität Tübingen, Otte, Sebastian/Universität Tübingen, Oladyshkin, Sergey/Universität Stuttgart, Butz, Martin V./Universität Tübingen, Nowak, Wolfgang/Universität Stuttgart}, author = {Praditia, Timothy and Karlbauer, Matthias and Otte, Sebastian and Oladyshkin, Sergey and Butz, Martin V. and Nowak, Wolfgang}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2298ae1b8f482bc7e64c1fbad0cf9466f/puma-wartung}, doi = {10.18419/darus-3249}, howpublished = {Dataset}, interhash = {a0a6bb52cf5e21524237ef311b4e4733}, intrahash = {298ae1b8f482bc7e64c1fbad0cf9466f}, keywords = {}, note = {Related to: Praditia, T., Karlbauer, M., Otte, S., Oladyshkin, S., Butz, M.V., Nowak, W.: Learning Groundwater Contaminant Diffusion-Sorption Processes with a Finite Volume Neural Network. Earth and Space Science Open Archive (2022). doi: 10.1002/essoar.10511934.1}, orcid-numbers = {Praditia, Timothy/0000-0003-3619-9122, Karlbauer, Matthias/0000-0002-4509-7921, Otte, Sebastian/0000-0002-0305-0463, Oladyshkin, Sergey/0000-0003-4676-5685, Butz, Martin V./0000-0002-8120-8537, Nowak, Wolfgang/0000-0003-2583-8865}, timestamp = {2023-08-31T13:02:28.000+0200}, title = {Replication Data for: Learning Groundwater Contaminant Diffusion-Sorption Processes with a Finite Volume Neural Network}, year = 2022 }