91 to 100 of 213 Results
Jun 20, 2023 - Usability and Sustainability of Simulation Software
Willeke, Leonard; Schneider, David; Uekermann, Benjamin, 2023, "Reproduction data for: A preCICE-FMI Runner to Couple FMUs to PDE-Based Simulations", https://doi.org/10.18419/DARUS-3549, DaRUS, V1
Input files and results for two simulation examples using the preCICE-FMI runner as described in "A preCICE-FMI Runner to Couple FMUs to PDE-Based Simulations". First, the simulation of a partitioned mass-spring oscillator system is considered. All model equations are based on ODEs. The coupled simulation can be executed with a FMU models, a Python... |
Jun 7, 2023 - Usability and Sustainability of Simulation Software
Willeke, Leonard, 2023, "Replication Data for: A preCICE-FMI Runner to couple controller models to PDEs", https://doi.org/10.18419/DARUS-3408, DaRUS, V1
A preCICE-FMI Runner was developed during the Master's Thesis "A preCICE-FMI Runner to couple controller models to PDEs". It couples models using the FMI standard with other simulation programs via the coupling library preCICE. This data set contains the code and casefiles presented in the thesis, as well as instructions to replicate the results. F... |
Jun 6, 2023 - Projects without PN Affiliation
Herkert, Robin, 2023, "Replication Code for: Randomized Symplectic Model Order Reduction for Hamiltonian Systems", https://doi.org/10.18419/DARUS-3519, DaRUS, V1
This dataset includes the code to reproduce the results from the paper titled "Randomized Symplectic Model Order Reduction for Hamiltonian Systems". In this paper randomized symplectic basis generation techniques are introduced. The numerical experiments where error decay rates and runtimes from the randomized methods and the classical, non-randomi... |
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 of preCICE used throughout this thesis. The thesis revolves around GPU... |
May 26, 2023 - Materials Design
Gubaev, Konstantin; Zaverkin, Viktor; Srinivasan, Prashanth; Duff, Andrew; Kästner, Johannes; Grabowski, Blazej, 2023, "Data for: Performance of two complementary machine-learned potentials in modelling chemically complex systems", https://doi.org/10.18419/DARUS-3516, DaRUS, V1
Data for the publication "Performance of two complementary machine-learned potentials in modelling chemically complex systems", npj. Comp. Mat. This data set contains the datasets of structures in cfg and npz formats INCAR file which was used for VASP calculations python script for reading npz format These are essentially the 2-, 3-, and 4-componen... |
Schmitt, Marvin, 2023, "Replication Code for: Meta-Uncertainty in Bayesian Model Comparison", https://doi.org/10.18419/DARUS-3514, DaRUS, V1, UNF:6:zUDr3KGdcaDCy+jFtcz8lA== [fileUNF]
This dataverse contains the code for the paper Meta-Uncertainty in Bayesian Model Comparison: https://doi.org/10.48550/arXiv.2210.07278 Note that the R code is structured as a package, thus requiring a local installation with subsequent loading via library(MetaUncertaintyPaper). The experiments from the accompanying paper (see below) are implemente... |
May 15, 2023 - PN 4-7
Baier, Alexandra; Frank, Daniel, 2023, "deepsysid: System Identification Toolkit for Multistep Prediction using Deep Learning", https://doi.org/10.18419/DARUS-3455, DaRUS, V1
deepsysid is a system identification toolkit for multistep prediction using deep learning and hybrid methods. The toolkit is easy to use. After you follow the instructions in the README, you will be able to download a dataset, run hyperparameter optimization and identify your best-performing multistep prediction models with just three commands: dee... |
May 15, 2023 - PN 4-7
Baier, Alexandra; Aspandi, Decky; Staab, Steffen, 2023, "Supplements for "ReLiNet: Stable and Explainable Multistep Prediction with Recurrent Linear Parameter Varying Networks""", https://doi.org/10.18419/DARUS-3457, DaRUS, V1
This repository contains the necessary scripts to reproduce the results from our paper "ReLiNet: Stable and Explainable Multistep Prediction with Recurrent Linear Parameter Varying Networks". See the README file for more information. The most current version of this software is available on Github. |
May 12, 2023 - Materials Design
Forslund, Axel; Jung, Jong Hyun; Srinivasan, Prashanth; Grabowski, Blazej, 2023, "Data for: Thermodynamic properties on the homologous temperature scale from direct upsampling: Understanding electron-vibration coupling and thermal vacancies in bcc refractory metals", https://doi.org/10.18419/DARUS-3339, DaRUS, V1
Data for the publication Thermodynamic properties on the homologous temperature scale from direct upsampling: Understanding electron-vibration coupling and thermal vacancies in bcc refractory metals, Phys. Rev. B 107, 174309 (2023). This data set contains - the training sets (VASP files), - the low moment-tensor potentials (MTPs) and high-MTPs, - t... |
May 4, 2023 - Publication: Particulate systems
Ruf, Matthias; Taghizadeh, Kianoosh; Steeb, Holger, 2023, "micro-XRCT data sets and in situ measured ultrasonic wave propagation of pre-stressed monodisperse rubber and glass particle mixtures with 10%, 20%, and 30% volume rubber content: samples 2 and 3", https://doi.org/10.18419/DARUS-3437, DaRUS, V1, UNF:6:LyvQPm+lMHvJ9Z4neyxihA== [fileUNF]
This dataset contains 12 micro X-ray Computed Tomography (micro-XRCT) data sets from scans of cylindrical particulate mixture samples (diameter 80 mm; unloaded height 80 mm) under different uniaxial compression loads. The samples consist of monodisperse stiff (glass) and soft (rubber) particle mixtures. Both particles have an identical diameter of... |
