EXC 2075 Project Networks

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Subject: Computer and Information Science Publication Year: 2022

1 to 10 of 12 Results

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 t...
Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v2] Aug 24, 2022 - PN 6 Holzmüller, David; Zaverkin, Viktor; Kästner, Johannes; Steinwart, Ingo, 2022, "Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v2]", https://doi.org/10.18419/darus-3110, DaRUS, V1 This dataset contains code and data for the second arXiv version of our paper "A Framework and Benchmark for Deep Batch Active Learning for Regression". The code can be used to reproduce the results, to benchmark new methods, or to apply the presented methods to new Deep Batch Ac...
Neck Reflex Behavior in Driving Simulator Experiments(Universität Stuttgart) Jun 27, 2022PN 2 Neck movement and injury risks during a car crash are influenced by reflexes, strength, and flexibility. Data sets are provided containing in-vivo measurements of neck kinematics and reflexes as participants are rapidly accelerated while driving a mechanically simulated car. Tors...
Code for: Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent Jun 20, 2022 - PN 6-3 Holzmüller, David; Steinwart, Ingo, 2022, "Code for: Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent", https://doi.org/10.18419/darus-2978, DaRUS, V1 This data set contains code used to generate figures and tables in our paper "Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent". The code is also available on GitHub. Information on the code and installation instructions can be found in the file README.md.
PN 4-7(Universität Stuttgart) May 16, 2022PN 4 Combining First Principles and Neural Network Models for Interpretable, High-Precision Multi-Step Predictions (InMotion)
preCICE Distribution Version v2202.0 Apr 21, 2022 - Usability and Sustainability of Simulation Software Chourdakis, Gerasimos; Davis, Kyle; Desai, Ishaan; Rodenberg, Benjamin; Schneider, David; Simonis, Frédéric; Uekermann, Benjamin; Firmbach, Max; Jaust, Alexander; Lorenz, Christopher; Martin, Boris; Olesen, Mark; Ziya Koseomur, Oguz, 2022, "preCICE Distribution Version v2202.0", https://doi.org/10.18419/darus-2613, DaRUS, V1 The preCICE distribution is the larger ecosystem around preCICE, which includes the core library, language bindings, adapters for popular solvers, tutorials, and vagrant files to prepare a virtual machine image. The compressed source files of this data set are only meant to archi...
Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v1] Apr 13, 2022 - PN 6 Holzmüller, David; Zaverkin, Viktor; Kästner, Johannes; Steinwart, Ingo, 2022, "Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v1]", https://doi.org/10.18419/darus-2615, DaRUS, V1 This dataset contains code and data for our paper "A Framework and Benchmark for Deep Batch Active Learning for Regression". The code can be used to reproduce the results, to benchmark new methods, or to apply the presented methods to new Deep Batch Active Learning problems. The...
Replication Data for: Fast Sparse Grid Operations using the Unidirectional Principle: A Generalized and Unified Framework Mar 29, 2022 - PN 6 Holzmüller, David, 2022, "Replication Data for: Fast Sparse Grid Operations using the Unidirectional Principle: A Generalized and Unified Framework", https://doi.org/10.18419/darus-1779, DaRUS, V1, UNF:6:aIyuHfDcWPT9LJvtkCge9w== [fileUNF] This dataset contains supplementary code for the paper Fast Sparse Grid Operations using the Unidirectional Principle: A Generalized and Unified Framework. The code is also provided on GitHub. Here, we additionally provide the runtime measurement data generated by the code, which...
Efficient three-material PLIC Implementation as Fortran Module Mar 1, 2022 - Subarea A7 Potyka, Johanna; Kromer, Johannes, 2022, "Efficient three-material PLIC Implementation as Fortran Module", https://doi.org/10.18419/darus-2488, DaRUS, V1 The dataset provides a Fortran module with the code for the efficient three-phase positioning method.
Test Setup of Turbine Blade Data Mapping Feb 14, 2022 - Usability and Sustainability of Simulation Software Simonis, Frédéric; Davis, Kyle; Uekermann, Benjamin, 2022, "Test Setup of Turbine Blade Data Mapping", https://doi.org/10.18419/darus-2491, DaRUS, V1, UNF:6:9SbHetN1EMzpQrXWO5FEeA== [fileUNF] Input data, scripts, and results of the data mapping tests of section 2.2 of "Chourdakis et al., preCICE v2 - A Sustainable and User-Friendly Coupling Library, 2022".

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 t...

Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v2]

Aug 24, 2022 - PN 6

Holzmüller, David; Zaverkin, Viktor; Kästner, Johannes; Steinwart, Ingo, 2022, "Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v2]", https://doi.org/10.18419/darus-3110, DaRUS, V1

This dataset contains code and data for the second arXiv version of our paper "A Framework and Benchmark for Deep Batch Active Learning for Regression". The code can be used to reproduce the results, to benchmark new methods, or to apply the presented methods to new Deep Batch Ac...

Neck Reflex Behavior in Driving Simulator Experiments(Universität Stuttgart)

Jun 27, 2022PN 2

Neck movement and injury risks during a car crash are influenced by reflexes, strength, and flexibility. Data sets are provided containing in-vivo measurements of neck kinematics and reflexes as participants are rapidly accelerated while driving a mechanically simulated car. Tors...

Code for: Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent

Jun 20, 2022 - PN 6-3

Holzmüller, David; Steinwart, Ingo, 2022, "Code for: Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent", https://doi.org/10.18419/darus-2978, DaRUS, V1

This data set contains code used to generate figures and tables in our paper "Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent". The code is also available on GitHub. Information on the code and installation instructions can be found in the file README.md.

PN 4-7(Universität Stuttgart)

May 16, 2022PN 4

Combining First Principles and Neural Network Models for Interpretable, High-Precision Multi-Step Predictions (InMotion)

preCICE Distribution Version v2202.0

Apr 21, 2022 - Usability and Sustainability of Simulation Software

Chourdakis, Gerasimos; Davis, Kyle; Desai, Ishaan; Rodenberg, Benjamin; Schneider, David; Simonis, Frédéric; Uekermann, Benjamin; Firmbach, Max; Jaust, Alexander; Lorenz, Christopher; Martin, Boris; Olesen, Mark; Ziya Koseomur, Oguz, 2022, "preCICE Distribution Version v2202.0", https://doi.org/10.18419/darus-2613, DaRUS, V1

The preCICE distribution is the larger ecosystem around preCICE, which includes the core library, language bindings, adapters for popular solvers, tutorials, and vagrant files to prepare a virtual machine image. The compressed source files of this data set are only meant to archi...

Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v1]

Apr 13, 2022 - PN 6

Holzmüller, David; Zaverkin, Viktor; Kästner, Johannes; Steinwart, Ingo, 2022, "Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v1]", https://doi.org/10.18419/darus-2615, DaRUS, V1

This dataset contains code and data for our paper "A Framework and Benchmark for Deep Batch Active Learning for Regression". The code can be used to reproduce the results, to benchmark new methods, or to apply the presented methods to new Deep Batch Active Learning problems. The...

Replication Data for: Fast Sparse Grid Operations using the Unidirectional Principle: A Generalized and Unified Framework

Mar 29, 2022 - PN 6

Holzmüller, David, 2022, "Replication Data for: Fast Sparse Grid Operations using the Unidirectional Principle: A Generalized and Unified Framework", https://doi.org/10.18419/darus-1779, DaRUS, V1, UNF:6:aIyuHfDcWPT9LJvtkCge9w== [fileUNF]

This dataset contains supplementary code for the paper Fast Sparse Grid Operations using the Unidirectional Principle: A Generalized and Unified Framework. The code is also provided on GitHub. Here, we additionally provide the runtime measurement data generated by the code, which...

Efficient three-material PLIC Implementation as Fortran Module

Mar 1, 2022 - Subarea A7

Potyka, Johanna; Kromer, Johannes, 2022, "Efficient three-material PLIC Implementation as Fortran Module", https://doi.org/10.18419/darus-2488, DaRUS, V1

The dataset provides a Fortran module with the code for the efficient three-phase positioning method.

Test Setup of Turbine Blade Data Mapping

Feb 14, 2022 - Usability and Sustainability of Simulation Software

Simonis, Frédéric; Davis, Kyle; Uekermann, Benjamin, 2022, "Test Setup of Turbine Blade Data Mapping", https://doi.org/10.18419/darus-2491, DaRUS, V1, UNF:6:9SbHetN1EMzpQrXWO5FEeA== [fileUNF]

Input data, scripts, and results of the data mapping tests of section 2.2 of "Chourdakis et al., preCICE v2 - A Sustainable and User-Friendly Coupling Library, 2022".

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