131 to 140 of 293 Results
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 17, 2023PN 6
Meta-Uncertainty represents a fully probabilistic framework for quantifying the uncertainty over Bayesian posterior model probabilities (PMPs) using meta-models. Meta-models integrate simulated and observed data into a predictive distribution for new PMPs and help reduce overconfidence and estimate the PMPs in future replication studies. |
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. |
