31 to 40 of 134 Results
Jul 1, 2023 - Usability and Sustainability of Simulation Software
Schrader, Timo Pierre; Schneider, David; Uekermann, Benjamin, 2023, "Replication Data for: Data-Parallel Radial-Basis Function Interpolation in preCICE", https://doi.org/10.18419/darus-3574, DaRUS, V1
This dataset contains setup and result files for the performance measurements presented in Schneider et al. "Data-Parallel Radial-Basis Function Interpolation in preCICE". Furthermore, it contains the used snapshot of preCICE, ASTE, and Gingko. See the README for information on h... |
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 presen... |
Dec 15, 2023 - PN 2-3B
Santana Chacon, Pablo Filipe; Hammer, Maria; Wochner, Isabell; Walter, Johannes R.; Schmitt, Syn, 2023, "Replication Data for: A physiologically enhanced muscle spindle model: using a Hill-type model for extrafusal fibers as template for intrafusal fibers", https://doi.org/10.18419/darus-3796, DaRUS, V1
This code/data allows you reproduce the results of the paper: "A physiologically enhanced muscle spindle model: using a Hill-type model for extrafusal fibers as template for intrafusal fibers" by P. F. S. Chacon, M. Hammer, I. Wochner, J. R. Walter and S. Schmitt. Always cite the... |
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. |
Mar 2, 2023 - PN 1-6
Kelm, Mathis; Bringedal, Carina; Flemisch, Bernd, 2023, "Replication Data for phase-field contributions in level-set comparison study", https://doi.org/10.18419/darus-3359, DaRUS, V1, UNF:6:MsNXqcARspF5yuVdFLKT0Q== [fileUNF]
Primary and post-processed simulation data and visualization tools to reproduce the phase-field results presented in the related publication. The folders "simulation" and "effective_quantities" contain primary simulation results obtained with the code published in the DuMux-pub m... |
Reiser, Philipp; Aguilar, Javier Enrique; Guthke, Anneli; Bürkner, Paul-Christian, 2024, "Replication Code for: Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference", https://doi.org/10.18419/darus-4093, DaRUS, V1
This code allows to replicate key experiments from our paper: Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference. For further details, please refer to the README.md. |
Keim, Leon; Class, Holger, 2024, "Replication Code for: Rayleigh invariance allows the estimation of effective CO2 fluxes due to convective dissolution into water-filled fractures", https://doi.org/10.18419/darus-4089, DaRUS, V1
This dataset consists of software code associated with the publication titled "Rayleigh Invariance Enables Estimation of Effective CO2 Fluxes Resulting from Convective Dissolution in Water-Filled Fractures." It includes a Dockerimage that contains the precompiled code for immedia... |
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).... |
Steffen, Maier, 2023, "Registered Cars and Motorized Two- and Three-wheelers in Worldwide Countries", https://doi.org/10.18419/darus-3378, DaRUS, V1
Interactive plot of registered cars and motorized two- and three-wheelers in worldwide countries based the data set from the "WHO Global Status Report on Road Safety 2018" of the World Health Organization. |
Jan 24, 2022 - PN 6-6
Tkachev, Gleb, 2022, "PyPlant: A Python Framework for Cached Function Pipelines", https://doi.org/10.18419/darus-2249, DaRUS, V1
PyPlant is a simple coroutine-based framework for writing data processing pipelines. PyPlant's goal is to simplify caching of intermediate results in the pipeline and avoid re-running expensive early stages of the pipeline, when only the later stages have changed. |
