EXC 2075 Project Networks

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Subject: Mathematical Sciences Subject: Computer and Information Science

11 to 20 of 30 Results

Replication Data for: Efficient Application of Accelerator Cards for the Coupling Library preCICE Jun 5, 2023 - Usability and Sustainability of Simulation Software Schrader, Timo Pierre, 2023, "Replication Data for: Efficient Application of Accelerator Cards for the Coupling Library preCICE", https://doi.org/10.18419/darus-3404, DaRUS, V1 This dataset contains all testcase setup files and result files for the measurements presented in the Master's thesis with the title "Efficient Application of Accelerator Cards for the Coupling Library preCICE" (Author: Timo Pierre Schrader). Furthermore, it contains the version...
Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v3] Apr 5, 2023 - PN 6 Holzmüller, David; Zaverkin, Viktor; Kästner, Johannes; Steinwart, Ingo, 2023, "Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v3]", https://doi.org/10.18419/darus-3394, DaRUS, V1 This dataset contains code and data for the third 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 Act...
Gaussian active learning on multi-resolution arbitrary polynomial chaos emulator Feb 15, 2023 - PN 5 Kohlhaas, Rebecca; Kröker, Ilja; Oladyshkin, Sergey; Nowak, Wolfgang, 2023, "Gaussian active learning on multi-resolution arbitrary polynomial chaos emulator", https://doi.org/10.18419/darus-2829, DaRUS, V1 This folder contains the code for the aMR-PC toolbox by Ilja Kröker in the version used for the code in GALMAP_code. This toolbox was also used for Kröker et al. 2022 Link to current version of the toolbox here Data This folder contains inputs and simulated outputs of the CO_2 be...
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...
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.
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: Efficient Application of Accelerator Cards for the Coupling Library preCICE

Jun 5, 2023 - Usability and Sustainability of Simulation Software

Schrader, Timo Pierre, 2023, "Replication Data for: Efficient Application of Accelerator Cards for the Coupling Library preCICE", https://doi.org/10.18419/darus-3404, DaRUS, V1

This dataset contains all testcase setup files and result files for the measurements presented in the Master's thesis with the title "Efficient Application of Accelerator Cards for the Coupling Library preCICE" (Author: Timo Pierre Schrader). Furthermore, it contains the version...

Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v3]

Apr 5, 2023 - PN 6

Holzmüller, David; Zaverkin, Viktor; Kästner, Johannes; Steinwart, Ingo, 2023, "Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v3]", https://doi.org/10.18419/darus-3394, DaRUS, V1

This dataset contains code and data for the third 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 Act...

Gaussian active learning on multi-resolution arbitrary polynomial chaos emulator

Feb 15, 2023 - PN 5

Kohlhaas, Rebecca; Kröker, Ilja; Oladyshkin, Sergey; Nowak, Wolfgang, 2023, "Gaussian active learning on multi-resolution arbitrary polynomial chaos emulator", https://doi.org/10.18419/darus-2829, DaRUS, V1

This folder contains the code for the aMR-PC toolbox by Ilja Kröker in the version used for the code in GALMAP_code. This toolbox was also used for Kröker et al. 2022 Link to current version of the toolbox here Data This folder contains inputs and simulated outputs of the CO_2 be...

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...

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.

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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