DaRUS (University of Stuttgart)
Not a member yet
2037 research outputs found
Sort by
preCICE Distribution Version v2404.0
No full textThe 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 archive this specific version v2404.0 of the distribution and make it citable. If you want to use preCICE, please follow the information on the preCICE website.
For your first steps in preCICE, we particularly recommend the virtual machine, which contains all packages of the preCICE distribution, many supported solvers, and more useful tools.
For more information, also read the accompanying blog post
Replication Data for: Efficient Partition-of-Unity Radial-Basis Function Interpolation for Coupled Problems
No full textThis dataset contains software (preCICE v3.1.1 and ASTE) as well as setup files and instructions to reproduce the experiments of Schneider and Uekermann "Efficient Partition-of-Unity Radial-Basis Function Interpolation for Coupled Problems". We, moreover, provide the actual results. See the README for information on how to build and rerun the cases
Replication Data for: Partitioned Multiphysics Simulation of an Electrophysiological Three-Tendon-Biceps Model
No full textThis dataset contains software (preCICE v3.1.2, OpenDiHu, deal.II-based solver and ASTE) as well as setup files and instructions to reproduce the experiments "Partitioned Multiphysics Simulation of an Electrophysiological Three-Tendon-Biceps Model". The actual results of the experiments are also included.
See the README file for more information about the structure of the dataset and how to run the experiments.
This is version V2 of the data set, which contains a bugfix for the configuration files used in the compositional coupling scheme experiments (Chapter 4).</p
Supplemental Material for "Power Overwhelming: The One With the Oscilloscopes"
No full textSupplemental Material for "Power Overwhelming: The One With the Oscilloscopes".
Contains the aggregated energy consumption data from the experiments in the paper. The application under test was MegaMol with two OpenGL-based sphere rasterization rendering methods (data static on GPU, data streaming to GPU) and OptiX-based sphere ray tracing
Modell zur Vorhersage von Prozessparametern eines Laserprozesses auf Basis von RGB-Farbwerten
No full textKünstliches neuronales Netz mit zwei verdeckten Schichten mit 50 bzw. 25 Neuronen und einer ReLU-Aktivierungsfunktion. Die Ausgabeschicht hat eine sigmoide Aktivierungsfunktion. Für das Training wird ein Adam-Optimierer verwendet, der den mittleren quadratischen Fehler der Vorhersage gegenüber der erwarteten Ausgabe minimiert und so die Gewichte des Netzes bestimmt.
Eingänge zw. 0 und 1:
b: Blau im RGB-Farbraum
g: Grün im RGB-Farbraum
r: Blau im RGB-Farbraum
Ausgänge zw. 0 und 1:
hatch_um: Der Hatch-Abstand zwischen Scanlinien in µm
power_mw: Die Laserleistung in mW
pulse_us: Die Pulsrate in µs
speed_um_s: Die Scangeschwindigkeit in µm/s
Skaliert wie folgt:
r, b, g: von 0 bis 255 [-]
hatch_um: von 1 bis 100 in [µs]
power_mw: von 1000 bis 20000 in [mW]
pulse_us: von 2 bis 10 in [µs]
speed_um_s: von 41666 bis 3333333 in [µm/s]
Das Modell kann mit dem folgenden Python-Code ausgeführt werden:
from keras.models import load_model
def scale_value(x, new_min, new_max):
scaled_value = (x * (new_max - new_min)) + new_min
return scaled_value
r = 123
b = 231
g = 85
model = load_model('model.keras')
output = model.predict(np.array([b / 255, g / 255, r / 255]).reshape(1, -1))
hatch_um = int(scale_value(output[0][0], 1, 100))
power_mw = int(scale_value(output[0][1], 1000, 20000))
pulse_us = int(scale_value(output[0][2], 2, 10))
speed_um_s = int(scale_value(output[0][3], 41666, 3333333))
</pre
Data for: Phase transition in porous materials - Effects of material parameters and deformation regime on mass conservativity
No full textThis dataset contains the original results published in "Phase transition in porous materials: effects of material parameters and deformation regime on mass conservativity" (doi: 10.1007/s00466-024-02557-2).
Abstract:
Phase transition in porous materials is relevant within different engineering applications, such as freezing in saturated soil or pancake sea ice. Mathematical descriptions of such processes can be derived based on Biot’s consolidation theory or the Theory of Porous Media. Depending on parameters such as density ratio, permeability or compressibility of the solid matrix, either small or finite deformations occur. Numerical solution procedures for the general, finite deformation case, suffers from instabilities and high computational costs. Simplifications, assuming small deformations, increases stability and computational efficiency. Within this work shortcomings of simplified theories based on Biot and linearisations of the Theory of Porous Media (TPM) are systematically studied. In order to determine the interaction of the different model parameters a non-dimensional model for poro-elasticity is presented. Based on a characteristic test-case including phase-transition and consolidation, the simplified models are compared to the fully non-linear TPM, focusing on mass errors as well as the time behaviour of the solution. Taking further into account the efficiency of discretisation based on different primal variables and finite-element-spaces, a guideline for selecting an appropriate combination of model, kinematic assumption and discretisation scheme is presented.
The analysis can be found in sections 4.2-4.4 of the paper. This dataset is structured analogously:
"data_section-4.2": Mass errors for the 1D consolidation example solved analytical based on the Biot equations using different non-dimensional parameters and boundary conditions
"data_section-4.3": Influence of the different models (Biot, lTPM and TPM) on primal variables (displacement, pressure and volume fraction) and the approximation of the system mass for the speciefied cases 1-1 to 1-4 resp. 2-1 to 2-4
"data_section-4.4": Computational efficiency of different mixed formulation for the considered 1D problem
It seems to make the most sense to search the data via the tree structure view
Replication Package for "Digital Twins for Machine Tools: A Systematic Mapping Study"
No full textReplication package for the paper "Digital Twins for Machine Tools: A Systematic Mapping Study". It includes the meta data of all considered literature
VisRecall++: Analysing and Predicting Recallability of Information Visualisations from Gaze Behaviour (Dataset and Reproduction Data)
No full textThis dataset contains stimuli and collected participant data of VisRecall++. The structure of the dataset is described in the README-File.
Further, if you are interested in related codes of the publication, you can find a copy of the code repository (see Metadata for Research Software) within this dataset.
</p
Replication Data for: Stable and Mass-Conserving High-Dimensional Simulations with the Sparse Grid Combination Technique for Full HPC Systems and Beyond
No full textThese are input, processing, and output files for experiments that were performed exclusively for my dissertation "Stable and mass-conserving high-dimensional simulations with the sparse grid combination technique for full HPC systems and beyond". More structure can be seen when switching to "Tree" view.
Other data repositories are referenced directly from the publication.
To install DisCoTec, the spack package is recommended. See the DisCoTec github repository for reference.
The files starting with `generate_` can be used to generate a simulation folder for a DisCoTec experiment (and if the submit command is uncommented, the experiment is submitted to the batch queue). For this to work, the accompanying files (`ctparam`, `scheme_39.25_GiB.json`, `submit_*.sh`,`system_parameters_*.sh`) will have to be in the same folder as the generate script. You will need to adapt the script as well as the submit and `system_parameter_$.sh` files to match your system / credentials / file paths.
Once the experiments have run, the timings will be written as json files in each experiment folder. They can be extracted using `postprocess.sh`, which in turn calls `csv_from_folders.sh`.
The outputs I generated are the compressed experiment folders (ending in `.tar.gz`, and the csv files containing the time measurements as referenced in the thesis.
</p
Data for: "Understanding the Reducibility of CeO2 Catalyst Surfaces by Proton-Electron Transfer from CpCr(CO)3H"
No full textThis file contains titration results as well as IR, EPR, XRD, and XAS data of the reduced cerium oxide samples synthesized in the publication in the title. File structure is given in the accompanying readme files