Subject: Computer and Information Science
Funding Information Agency: Deutsche Forschungsgemeinschaft
1 to 10 of 11 Results
Kraus, Hamzeh; Hansen, Niels, 2021, "Supplementary material for 'Confinement Effects for Efficient Macrocyclization Reactions with Supported Cationic Molybdenum Imido Alkylidene N‑Heterocyclic Carbene Complexes'", https://doi.org/10.18419/darus-1752, DaRUS, V1
This dataset contains simulation input files in GROMACS format accompanying the mentioned publication. Structure, topology, and simulation parameter-files (directory Mdp) are provided for four simulations - a 2.5 nm pore with one catalyst molecule, a 2.5 nm pore with two catalyst... |
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... |
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. |
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... |
Mar 11, 2021 - C02: Upscaling of pore-scale processes involving microstructural evolution
Freiherr von Wolff, Lars, 2021, "The DUNE-Phasefield Module (release 1.0)", https://doi.org/10.18419/darus-1634, DaRUS, V1
The DUNE-Phasefield module contains local operators and grid function spaces for solving Cahn-Hilliard and Cahn-Hilliard-Navier-Stokes systems in a FEM framework. The module additionally provides option-classes for physical properties, multiple Cahn-Hilliard potentials, a timeste... |
Kraus, Hamzeh, 2021, "Supplementary material for 'PoreMS: A software tool for generating silica pore models with user-defined surface functionalisation and pore dimensions'", https://doi.org/10.18419/darus-1170, DaRUS, V1
This dataset contains Jupiter Notebooks and the necessary structure files needed for generating the pore systems discussed in the related publication. Additionally, high-resolution figures discussed in the paper are added. |
Feb 5, 2021 - B03: Heterogeneous multi-scale methods for two-phase flow in dynamically fracturing porous media
Burbulla, Samuel, 2021, "The DUNE MMesh Module (Release 1.2)", https://doi.org/10.18419/darus-1257, DaRUS, V1
The dune-mmesh module is an implementation of the DUNE grid interface that wraps CGAL triangulations in 2D and 3D. It is also capable to export a prescribed set of cell facets as a dim-1 interface grid and remesh the grid when moving this interface. |
Munz, Tanja; Schäfer, Noel; Blascheck, Tanja; Kurzhals, Kuno; Zhang, Eugene; Weiskopf, Daniel, 2020, "Supplemental Material for Comparative Visual Gaze Analysis for Virtual Board Games", https://doi.org/10.18419/darus-1130, DaRUS, V1
Supplemental material for "Comparative Visual Gaze Analysis for Virtual Board Games". Contains the source code, an executable file, an example data set, a video for demonstration of the analysis system and additional information about Go. The analysis system supports eye tracking... |
Jul 21, 2020 - PINN Dynamic System
Praditia, Timothy, 2020, "Trained ANN Parameters for Physics-inspired Artificial Neural Network for Dynamic System", https://doi.org/10.18419/darus-634, DaRUS, V1
This dataset contains four .xlsx files containing trained values of the ANN weights and biases, along with the hyperparameter values at the end of the training (with noisy dataset). These four files correspond to four different regularization methods. |
Jul 21, 2020 - PINN Dynamic System
Praditia, Timothy, 2020, "Input-Output Dataset for Physics-inspired Artificial Neural Network for Dynamic System", https://doi.org/10.18419/darus-633, DaRUS, V1
This dataset contains two .mat files, one pre-processed (direct simulation results) and the other one is with added noise. The simulated problem is a thermochemical energy storage problem using CaO/Ca(OH)2 as the material choice. This dataset is used as input-output data pairs ne... |
