EXC 2075 Project Networks

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Deposit Date: 2021 Publication Year: 2021

1 to 10 of 11 Results

Supplementary material for 'Umbrella sampling and double decoupling data for methanol binding to Candida antarctica lipase B' Nov 25, 2021 - Molecular Simulation Markthaler, Daniel; Hansen, Niels, 2021, "Supplementary material for 'Umbrella sampling and double decoupling data for methanol binding to Candida antarctica lipase B'", https://doi.org/10.18419/darus-2104, DaRUS, V1 This dataset contains all relevant simulation input files (topologies, coordinates, simulation parameters), generated simulation output (final configurations, time series of collective variables) together with scripts used for set-up and analysis of the umbrella sampling and doub...
Code for relative permeabilities for two-phase flow between parallel plates with slip conditions Nov 24, 2021 - PN 1-6 Schulz, Sebastian; Bringedal, Carina; Ackermann, Sina, 2021, "Code for relative permeabilities for two-phase flow between parallel plates with slip conditions", https://doi.org/10.18419/darus-2241, DaRUS, V1 This MATLAB code calculates relative permeabilities of two fluids flowing between two parallel plates, depending on their viscosities, saturation and boundary conditions at the top and bottom plates. The underlying assumptions behind the derivation are shown in the pdf "overview_...
Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments Oct 15, 2021 - PN 6 Zaverkin, Viktor; Holzmüller, David; Steinwart, Ingo; Kästner, Johannes, 2021, "Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments", https://doi.org/10.18419/darus-2136, DaRUS, V1 Code and documentation for the improved Gaussian Moments Neural Network (GM-NN). An updated version can be found on GitLab
Replication Data for: "S4: Self-Supervised learning of Spatiotemporal Similarity" Oct 5, 2021 - PN 6-6 Tkachev, Gleb, 2021, "Replication Data for: "S4: Self-Supervised learning of Spatiotemporal Similarity"", https://doi.org/10.18419/darus-2174, DaRUS, V1 We train a self-supervised siamese model that enables querying for similar behavior on spatiotemporal volumes. Here we provide the code and data needed to reproduce the representative figures of the paper. See the notes and the included readme file for details.
preCICE Distribution Version v2104.0 Sep 27, 2021 - Usability and Sustainability of Simulation Software Chourdakis, Gerasimos; Davis, Kyle; Rodenberg, Benjamin; Schulte, Miriam; Simonis, Frédéric; Uekermann, Benjamin; Abrams, Georg; Bungartz, Hans-Joachim; Cheun Yau, Lucia; Desai, Ishaan; Eder, Konrad; Hertrich, Richard; Lindner, Florian; Rusch, Alexander; Sashko, Dmytro; Schneider, David; Totounferoush, Amin; Volland, Dominik; Vollmer, Peter; Ziya Koseomur, Oguz, 2021, "preCICE Distribution Version v2104.0", https://doi.org/10.18419/darus-2125, 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...
Visual Analytics System for Hidden States in Recurrent Neural Networks Sep 10, 2021 - PN 6-4 Munz, Tanja; Garcia, Rafael; Weiskopf, Daniel, 2021, "Visual Analytics System for Hidden States in Recurrent Neural Networks", https://doi.org/10.18419/darus-2052, DaRUS, V1 Source code of our visual analytics system for the interpretation of hidden states in recurrent neural networks. This project contains source code for preprocessing data and the visual analytics system. Additionally, we added precomputed data for immediate use in the visual analy...
Simulation input scripts for "Electronic screening using a virtual Thomas-Fermi fluid for predicting wetting and phase transitions of ionic liquids at metal surfaces" Aug 20, 2021 - Paper Nature Materials 2021 Schlaich, Alexander, 2021, "Simulation input scripts for "Electronic screening using a virtual Thomas-Fermi fluid for predicting wetting and phase transitions of ionic liquids at metal surfaces"", https://doi.org/10.18419/darus-2115, DaRUS, V1 This dataset includes the basic simulation scripts needed in order to reproduce the data shown in "Electronic screening using a virtual Thomas-Fermi fluid for predicting wetting and phase transitions of ionic liquids at metal surfaces". The folder structure corresponds to the ind...
NMTVis - Trained Models for our Visual Analytics System May 26, 2021 - PN 6-4 Munz, Tanja; Väth, Dirk; Kuznecov, Paul; Vu, Ngoc Thang; Weiskopf, Daniel, 2021, "NMTVis - Trained Models for our Visual Analytics System", https://doi.org/10.18419/darus-1850, DaRUS, V1 Trained models and vocabulary files for the use in our visual analytics system NMTVis. There are models for German to English and vice versa available for an LSTM-based and the Transformer architecture.
NMTVis - Neural Machine Translation Visualization System May 26, 2021 - PN 6-4 Munz, Tanja; Väth, Dirk; Kuznecov, Paul; Vu, Ngoc Thang; Weiskopf, Daniel, 2021, "NMTVis - Neural Machine Translation Visualization System", https://doi.org/10.18419/darus-1849, DaRUS, V1 NMTVis is a web-based visual analytics system to analyze, understand, and correct translations generated with neural machine translation. First, a document can be translated using a neural machine translation model (we support an LSTM-based and the Transformer architecture). Afte...
A Data Set for Research on Data-based Methods for an Omnidirectional Mobile Robot May 21, 2021 - PN 4-4 Eschmann, Hannes, 2021, "A Data Set for Research on Data-based Methods for an Omnidirectional Mobile Robot", https://doi.org/10.18419/darus-1845, DaRUS, V1 The intend of this data set is the cooperation within SimTech. It will be particularly interesting for data-based modeling and control which is a key area of the research of project network 4. We are proud to provide real-world data, which is essential for the benchmark of any da...

Supplementary material for 'Umbrella sampling and double decoupling data for methanol binding to Candida antarctica lipase B'

Nov 25, 2021 - Molecular Simulation

Markthaler, Daniel; Hansen, Niels, 2021, "Supplementary material for 'Umbrella sampling and double decoupling data for methanol binding to Candida antarctica lipase B'", https://doi.org/10.18419/darus-2104, DaRUS, V1

This dataset contains all relevant simulation input files (topologies, coordinates, simulation parameters), generated simulation output (final configurations, time series of collective variables) together with scripts used for set-up and analysis of the umbrella sampling and doub...

Code for relative permeabilities for two-phase flow between parallel plates with slip conditions

Nov 24, 2021 - PN 1-6

Schulz, Sebastian; Bringedal, Carina; Ackermann, Sina, 2021, "Code for relative permeabilities for two-phase flow between parallel plates with slip conditions", https://doi.org/10.18419/darus-2241, DaRUS, V1

This MATLAB code calculates relative permeabilities of two fluids flowing between two parallel plates, depending on their viscosities, saturation and boundary conditions at the top and bottom plates. The underlying assumptions behind the derivation are shown in the pdf "overview_...

Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments

Oct 15, 2021 - PN 6

Zaverkin, Viktor; Holzmüller, David; Steinwart, Ingo; Kästner, Johannes, 2021, "Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments", https://doi.org/10.18419/darus-2136, DaRUS, V1

Code and documentation for the improved Gaussian Moments Neural Network (GM-NN). An updated version can be found on GitLab

Replication Data for: "S4: Self-Supervised learning of Spatiotemporal Similarity"

Oct 5, 2021 - PN 6-6

Tkachev, Gleb, 2021, "Replication Data for: "S4: Self-Supervised learning of Spatiotemporal Similarity"", https://doi.org/10.18419/darus-2174, DaRUS, V1

We train a self-supervised siamese model that enables querying for similar behavior on spatiotemporal volumes. Here we provide the code and data needed to reproduce the representative figures of the paper. See the notes and the included readme file for details.

preCICE Distribution Version v2104.0

Sep 27, 2021 - Usability and Sustainability of Simulation Software

Chourdakis, Gerasimos; Davis, Kyle; Rodenberg, Benjamin; Schulte, Miriam; Simonis, Frédéric; Uekermann, Benjamin; Abrams, Georg; Bungartz, Hans-Joachim; Cheun Yau, Lucia; Desai, Ishaan; Eder, Konrad; Hertrich, Richard; Lindner, Florian; Rusch, Alexander; Sashko, Dmytro; Schneider, David; Totounferoush, Amin; Volland, Dominik; Vollmer, Peter; Ziya Koseomur, Oguz, 2021, "preCICE Distribution Version v2104.0", https://doi.org/10.18419/darus-2125, 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...

Visual Analytics System for Hidden States in Recurrent Neural Networks

Sep 10, 2021 - PN 6-4

Munz, Tanja; Garcia, Rafael; Weiskopf, Daniel, 2021, "Visual Analytics System for Hidden States in Recurrent Neural Networks", https://doi.org/10.18419/darus-2052, DaRUS, V1

Source code of our visual analytics system for the interpretation of hidden states in recurrent neural networks. This project contains source code for preprocessing data and the visual analytics system. Additionally, we added precomputed data for immediate use in the visual analy...

Simulation input scripts for "Electronic screening using a virtual Thomas-Fermi fluid for predicting wetting and phase transitions of ionic liquids at metal surfaces"

Aug 20, 2021 - Paper Nature Materials 2021

Schlaich, Alexander, 2021, "Simulation input scripts for "Electronic screening using a virtual Thomas-Fermi fluid for predicting wetting and phase transitions of ionic liquids at metal surfaces"", https://doi.org/10.18419/darus-2115, DaRUS, V1

This dataset includes the basic simulation scripts needed in order to reproduce the data shown in "Electronic screening using a virtual Thomas-Fermi fluid for predicting wetting and phase transitions of ionic liquids at metal surfaces". The folder structure corresponds to the ind...

NMTVis - Trained Models for our Visual Analytics System

May 26, 2021 - PN 6-4

Munz, Tanja; Väth, Dirk; Kuznecov, Paul; Vu, Ngoc Thang; Weiskopf, Daniel, 2021, "NMTVis - Trained Models for our Visual Analytics System", https://doi.org/10.18419/darus-1850, DaRUS, V1

Trained models and vocabulary files for the use in our visual analytics system NMTVis. There are models for German to English and vice versa available for an LSTM-based and the Transformer architecture.

NMTVis - Neural Machine Translation Visualization System

May 26, 2021 - PN 6-4

Munz, Tanja; Väth, Dirk; Kuznecov, Paul; Vu, Ngoc Thang; Weiskopf, Daniel, 2021, "NMTVis - Neural Machine Translation Visualization System", https://doi.org/10.18419/darus-1849, DaRUS, V1

NMTVis is a web-based visual analytics system to analyze, understand, and correct translations generated with neural machine translation. First, a document can be translated using a neural machine translation model (we support an LSTM-based and the Transformer architecture). Afte...

A Data Set for Research on Data-based Methods for an Omnidirectional Mobile Robot

May 21, 2021 - PN 4-4

Eschmann, Hannes, 2021, "A Data Set for Research on Data-based Methods for an Omnidirectional Mobile Robot", https://doi.org/10.18419/darus-1845, DaRUS, V1

The intend of this data set is the cooperation within SimTech. It will be particularly interesting for data-based modeling and control which is a key area of the research of project network 4. We are proud to provide real-world data, which is essential for the benchmark of any da...

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