PN 5

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

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881 to 890 of 1,005 Results

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.
langmuir.rar Nov 2, 2022 - Replication Data for: Learning Groundwater Contaminant Diffusion-Sorption Processes with a Finite Volume Neural Network RAR Archive - 4.6 MB - MD5: 13fc5f6002a31acb194132b43364068d Dissolved and total contaminant concentration data generated with the Langmuir sorption isotherm.
linear.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: d3d4f64769bbcc9f269ae8f7da229e86 Dissolved and total contaminant concentration data generated with the linear sorption isotherm.
Replication Data for: The Method of Forced Probabilities: a Computation Trick for Bayesian Model Evidence Oct 26, 2022 - Modeling Strategies for Gas migration in Subsurface Banerjee, Ishani; Walter, Peter, 2022, "Replication Data for: The Method of Forced Probabilities: a Computation Trick for Bayesian Model Evidence", https://doi.org/10.18419/DARUS-2815, DaRUS, V1 This dataset contains the codes used for implementing the method of forced probabilities of the manuscript: The Method of Forced Probabilities: A Computation Trick for Bayesian Model Evidence. Here, one can find the codes of implementation of the trick on stochastic invasion percolation (SIP) models discussed in the manuscript; it can be used by th...
add_evaluations_realData.m Oct 26, 2022 - Replication Data for: The Method of Forced Probabilities: a Computation Trick for Bayesian Model Evidence Plain Text - 4.1 KB - MD5: 6fee5b73c68cdf41aa2e07b588b49d08

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.

langmuir.rar

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

RAR Archive - 4.6 MB -

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

linear.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 linear sorption isotherm.

Replication Data for: The Method of Forced Probabilities: a Computation Trick for Bayesian Model Evidence

Oct 26, 2022 - Modeling Strategies for Gas migration in Subsurface

Banerjee, Ishani; Walter, Peter, 2022, "Replication Data for: The Method of Forced Probabilities: a Computation Trick for Bayesian Model Evidence", https://doi.org/10.18419/DARUS-2815, DaRUS, V1

This dataset contains the codes used for implementing the method of forced probabilities of the manuscript: The Method of Forced Probabilities: A Computation Trick for Bayesian Model Evidence. Here, one can find the codes of implementation of the trick on stochastic invasion percolation (SIP) models discussed in the manuscript; it can be used by th...

add_evaluations_realData.m

Oct 26, 2022 - Replication Data for: The Method of Forced Probabilities: a Computation Trick for Bayesian Model Evidence

Plain Text - 4.1 KB -

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