1 to 10 of 22 Results
Feb 14, 2022 - Usability and Sustainability of Simulation Software
Simonis, Frédéric; Davis, Kyle; Uekermann, Benjamin, 2022, "Test Setup of Turbine Blade Data Mapping", https://doi.org/10.18419/darus-2491, DaRUS, V1, UNF:6:9SbHetN1EMzpQrXWO5FEeA== [fileUNF]
Input data, scripts, and results of the data mapping tests of section 2.2 of "Chourdakis et al., preCICE v2 - A Sustainable and User-Friendly Coupling Library, 2022". |
Jan 17, 2023 - PN 7-6
Rodegast, Philipp; Maier, Steffen; Kneifl, Jonas; Fehr, Jörg, 2023, "Simulation Data from Motorcycle Sensors in Operational and Crash Scenarios", https://doi.org/10.18419/darus-3301, DaRUS, V1, UNF:6:rnJlYpzgwi2nYAtNG7jBtA== [fileUNF]
This dataset provides time-dependent simulation results from high-fidelity motorcycle body crash scenarios. The set contains the angular as well as linear positions, velocities, and accelerations of different parts of the motorcycle. In addition, force and contact sensor signals... |
Davis, Kyle; Schulte, Miriam, 2022, "Replication Data for: Radial basis function interpolation with partition of unity for PyRBF", https://doi.org/10.18419/darus-3183, DaRUS, V1, UNF:6:n9rIOI84ZodNZYhB8p26GQ== [fileUNF]
The dataset provides the PyRBF python code to perform partition of unity radial basis function interpolation. This includes all meshes in VTK format and a post processing folder to generate the figures from provided results. The README provides further instructions. All software... |
Rettberg, Johannes; Wittwar, Dominik; Buchfink, Patrick; Brauchler, Alexander; Ziegler, Pascal; Fehr, Jörg; Haasdonk, Bernard, 2023, "Replication Data for: Port-Hamiltonian Fluid-Structure Interaction Modeling and Structure-Preserving Model Order Reduction of a Classical Guitar", https://doi.org/10.18419/darus-3248, DaRUS, V1
This dataset includes the system matrices in velocity formulation and a parallel coordinates plot of the sensitivity analysis from the paper titled "Port-Hamiltonian Fluid-Structure Interaction Modeling and Structure-Preserving Model Order Reduction of a Classical Guitar". The sy... |
Nov 2, 2022 - PN 5-6
Praditia, Timothy; Karlbauer, Matthias; Otte, Sebastian; Oladyshkin, Sergey; Butz, Martin V.; Nowak, Wolfgang, 2022, "Replication Data for: Learning Groundwater Contaminant Diffusion-Sorption Processes with a Finite Volume Neural Network", https://doi.org/10.18419/darus-3249, DaRUS, V1
This dataset contains diffusion-sorption data, generated with numerical simulation based on three different sorption isotherms, namely the linear, Freundlich, and Langmuir isotherms. This dataset is used to train, validate, and test all the deep learning models that are used in t... |
Oct 26, 2022 - Projects without PN Affiliation
Davis, Kyle; Schulte, Miriam, 2022, "Replication Data for: Geothermal-ML - predicting thermal plume from groundwater heat pumps", https://doi.org/10.18419/darus-3184, DaRUS, V1
This dataset provides all python3 code necessary to train convolutional neural networks built with pyTorch, as well as the data to train the networks. A README file is provided with instructions for accessing the training data, viewing the alread computed models, and all software... |
Oct 26, 2022 - Projects without PN Affiliation
Davis, Kyle; Schulte, Miriam, 2022, "REG-30 model to replicate simulation results for the GEO.KW project", https://doi.org/10.18419/darus-3195, DaRUS, V1
This data repository contains the REG-30 model for the GEO.KW project. The dataset contains the PFLOTRAN and urbs software and simulation setup. Instructions for building the spack environment and running the simulation are provided in the README. |
Apr 21, 2022 - Usability and Sustainability of Simulation Software
Chourdakis, Gerasimos; Davis, Kyle; Desai, Ishaan; Rodenberg, Benjamin; Schneider, David; Simonis, Frédéric; Uekermann, Benjamin; Firmbach, Max; Jaust, Alexander; Lorenz, Christopher; Martin, Boris; Olesen, Mark; Ziya Koseomur, Oguz, 2022, "preCICE Distribution Version v2202.0", https://doi.org/10.18419/darus-2613, 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... |
Feb 20, 2023 - PN 6
Zaverkin, Viktor; Holzmüller, David; Bonfirraro, Luca; Kästner, Johannes, 2023, "Pre-trained and fine-tuned ANI models for: Transfer learning for chemically accurate interatomic neural network potentials", https://doi.org/10.18419/darus-3299, DaRUS, V1
Pre-trained and fine-tuned ANI models using the Gaussian Moments Neural Network (GM-NN) approach. Code for GM-NN implemented within the Tensorflow framework, including the respective documentation and tutorials, can be found on GitLab. The data represents TensorFlow v2 checkpoint... |
Nov 30, 2023 - SciML PDE Benchmark
Takamoto, Makoto; Praditia, Timothy; Leiteritz, Raphael; MacKinlay, Dan; Alesiani, Francesco; Pflüger, Dirk; Niepert, Mathias, 2022, "PDEBench Pretrained Models", https://doi.org/10.18419/darus-2987, DaRUS, V2
This dataset contains the pretrained baseline models, namely FNO, U-Net, and PINN. These models are trained on different PDEs, such as 1D advection, 1D Burgers', 1D and 2D diffusion-reaction, 1D diffusion-sorption, 1D, 2D, and 3D compressible Navier-Stokes, 2D Darcy flow, and 2D... |
