PN 5

SimTech EXC 2075 Project Network 5 "On-the-fly model modification, error control, and simulation adaptivity"

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Std_Nr1_No1_it99.png Feb 15, 2023 - Gaussian active learning on multi-resolution arbitrary polynomial chaos emulator PNG Image - 124.4 KB - MD5: 51edd1dcb7b17e067741620dd2e44d60
Std_Nr1_No1_it9.png Feb 15, 2023 - Gaussian active learning on multi-resolution arbitrary polynomial chaos emulator PNG Image - 135.7 KB - MD5: ad8b380c38d988a5832a9a630f772161
Std_Nr1_No1_it9.png Feb 15, 2023 - Gaussian active learning on multi-resolution arbitrary polynomial chaos emulator PNG Image - 136.4 KB - MD5: 25bdfad9ab95ce89a5bcf24ebd4ff3b1
Std_Nr1_No1_it9.png Feb 15, 2023 - Gaussian active learning on multi-resolution arbitrary polynomial chaos emulator PNG Image - 117.9 KB - MD5: 8494af2895341330fffbd7360341f018
std.txt Feb 15, 2023 - Gaussian active learning on multi-resolution arbitrary polynomial chaos emulator Plain Text - 20.8 KB - MD5: a1d43702326704c931d887c98ac84f7e
twostep.py Feb 15, 2023 - Gaussian active learning on multi-resolution arbitrary polynomial chaos emulator Python Source Code - 8.5 KB - MD5: f109de5ac86bbc055a60d39ac86dc8cc
utils.py Feb 15, 2023 - Gaussian active learning on multi-resolution arbitrary polynomial chaos emulator Python Source Code - 5.6 KB - MD5: b18e2e964207c1c5631790e41f5d2bb9
wavetools.py Feb 15, 2023 - Gaussian active learning on multi-resolution arbitrary polynomial chaos emulator Python Source Code - 8.9 KB - MD5: ed55405b58aef65d2662255ddd8d9cc4
Replication Data for: Learning Groundwater Contaminant Diffusion-Sorption Processes with a Finite Volume Neural Network Nov 2, 2022 - PN 5-6 Praditia, Timothy; Karlbauer, Matthias; Otte, Sebastian; Oladyshkin, Sergey; Butz, Martin V.; Nowak, Wolfgang, 2022, "Replication Data for: Learning Groundwater Contaminant Diffusion-Sorption Processes with a Finite Volume Neural Network", https://doi.org/10.18419/DARUS-3249, DaRUS, V1 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 Pr...
freundlich.rar Nov 2, 2022 - Replication Data for: Learning Groundwater Contaminant Diffusion-Sorption Processes with a Finite Volume Neural Network RAR Archive - 4.9 MB - MD5: 2bbf50daf95d7202805cae40478ab4f6 Dissolved and total contaminant concentration data generated with the Freundlich sorption isotherm.

Std_Nr1_No1_it99.png

Feb 15, 2023 - Gaussian active learning on multi-resolution arbitrary polynomial chaos emulator

PNG Image - 124.4 KB -

Std_Nr1_No1_it9.png

Feb 15, 2023 - Gaussian active learning on multi-resolution arbitrary polynomial chaos emulator

PNG Image - 135.7 KB -

Std_Nr1_No1_it9.png

Feb 15, 2023 - Gaussian active learning on multi-resolution arbitrary polynomial chaos emulator

PNG Image - 136.4 KB -

Std_Nr1_No1_it9.png

Feb 15, 2023 - Gaussian active learning on multi-resolution arbitrary polynomial chaos emulator

PNG Image - 117.9 KB -

std.txt

Feb 15, 2023 - Gaussian active learning on multi-resolution arbitrary polynomial chaos emulator

Plain Text - 20.8 KB -

twostep.py

Feb 15, 2023 - Gaussian active learning on multi-resolution arbitrary polynomial chaos emulator

Python Source Code - 8.5 KB -

utils.py

Feb 15, 2023 - Gaussian active learning on multi-resolution arbitrary polynomial chaos emulator

Python Source Code - 5.6 KB -

wavetools.py

Feb 15, 2023 - Gaussian active learning on multi-resolution arbitrary polynomial chaos emulator

Python Source Code - 8.9 KB -

Replication Data for: Learning Groundwater Contaminant Diffusion-Sorption Processes with a Finite Volume Neural Network

Nov 2, 2022 - PN 5-6

Praditia, Timothy; Karlbauer, Matthias; Otte, Sebastian; Oladyshkin, Sergey; Butz, Martin V.; Nowak, Wolfgang, 2022, "Replication Data for: Learning Groundwater Contaminant Diffusion-Sorption Processes with a Finite Volume Neural Network", https://doi.org/10.18419/DARUS-3249, DaRUS, V1

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 Pr...

freundlich.rar

Nov 2, 2022 - Replication Data for: Learning Groundwater Contaminant Diffusion-Sorption Processes with a Finite Volume Neural Network

RAR Archive - 4.9 MB -

Dissolved and total contaminant concentration data generated with the Freundlich sorption isotherm.

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