DaRUS (University of Stuttgart)
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2037 research outputs found
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exaFOAM Microbenchmark MB7 - Viscoelastic Lid-driven Cavity
No full textThis work is part of the exaFOAM project, aimed at optimizing the open-source CFD software OpenFOAM for massively parallel HPC architectures and overcoming performance bottlenecks. The viscoelastic lid-driven cavity case was designed to evaluate the viscoelasticFluidFoam solver's numerical implementation and computational performance within a simple geometry and orthogonal meshes, without having to deal with issues pertaining to non-orthogonal meshes. The problem is modelled considering the isothermal and incompressible 2D flow of a Giesekus viscoelastic fluid. Detailed information and case setup can be found in the README.md file contained in the case setup file
Data for: 'EnzymeML-based modeling workflow: from raw data to kinetic parameters'
No full textKinetic parameter estimates for small laccase (SLAC) catalyzed oxidation of ABTS, investigated across the temperature range between 25 °C and 45°C. This dataset contains the following files:
Unprocessed absorption data from the respective enzyme assays (.txt)
Derived calibration data for the respective calibration measurements (.json)
EnzymeML Documents (.omex) with
Experimental conditions
Measurement data
Estimated parameters and applied models
Analysis notebook (.ipynb)
Python requirements.txt for all dependencies
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Code for Training and Testing CNVVE
No full textThis dataset consists of files used for training and testing the CNVVE Dataset. This dataset consists of 950 audio samples encompassing six distinct classes of voice expressions. These expressions were collected from 42 generous individuals who donated their voice recordings for the study. By making the dataset publicly accessible, we hope to facilitate further research and development of computational methods for non-verbal voice-based interactions.
For more info, please check the paper or feel free to contact the authors for any inquiries.</p
Performance Data for the Visualisation of Time-Dependent Particles using DirectStorage
No full textResults of a series of performance measurements (frame times) to determine the impact of using the DirectStorage API for rendering time-dependent particle data sets in contrast to using traditional POSIX-style I/O APIs
Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements"
No full textReplication code and experiment result data for training Quantum Neural Networks with entangled data using one-dimensional projectors as observables.
This is the version of the code that was used to generate the experiment results in the related publication.
Experiments:
- exp_inf_coeffvariation.py: Trains QNNs using training samples of varying Schmidt rank with fixed vector as Schmidt basis state. Varies the associated Schmidt coefficient.
- exp_inf_random.py: Trains QNNs using random training data.
Experiment results:
- exp_inf_coeffvariation.zip and exp_inf_random.zip contain the raw experiment results for both experiments.
- For each combination of controlled variables there is one directory containing the result of all 20 runs of the training process.
- The results for each run are comprised of 3 files:
- [id]_losses.npy: The loss during the training process
- [id]_params.npy: The parameters of the QNN after the training process.
- [id]_V.npy: The trained QNN exported as a 2^4 * 2^4 unitary matrix.
Analysis of data (data_extraction.py):
- Computes means and standard deviation of various risk measures and saves the results
Plots (plot_obs_risk.py):
- Plots the risk w.r.t. the observable for both experiments based on the analysed data obtained from data_extraction.py.
- Generates plot_coeffvariation.pdf and plot_random.pdf.
</p
Source Code: A Chemistry Load Balancing Model for OpenFOAM
No full textEfficient simulation tools are crucial for studying complex systems such as
reacting flows where computational costs of computing chemical reaction rates can vastly exceed the costs for the integration of the convective and diffusive transport terms. Load imbalance in parallel computing poses a significant challenge for massively parallel reacting flow simulations. In response, a novel load balancing library has been developed to enhance OpenFOAM’s solver performance in parallel environments. This library seamlessly integrates with OpenFOAM, offering ease of use and applicability to any OpenFOAM reacting solver incorporating finite-chemistry. In addition, it supports the standard and the dynamic adaptive chemistry model (TDAC) of OpenFOAM. The newly developed load-balanced standard and TDAC models address significant load imbalances by exchanging information between processes via MPI calls and tracking ODE solution times on a cell level. The TDAC model introduces dual tables on each core and enables immediate addition of computed solutions, enhancing computational efficiency. Validation on various test cases, including simulations on the HLRS Hawk supercomputer up to 8000 cores, confirm identical results compared to the original unbalanced models, with notable speed-up factors of up to 6 for the standard and 5 for the TDAC model. Despite non-linear scaling at lower cell count per processor, load-balanced models consistently outperform unbalanced counterparts, making them the preferred choice for reacting flow simulations in OpenFOAM.
See more information about the usage of this library in the README.</p
Dataset for "How Deep Is Your Gaze? Leveraging Distance in Image-Based Gaze Analysis"
No full textThis dataset was recorded in an AR environment comprised of three physical and three virtual scene objects. Four participants were instructed to gaze at the six objects from different depth levels (50cm, 150cm, 300cm) in two orders (left-to-right, right-to-left). There are seven trials per condition, which equals 42 recordings in total. More details can be found in the README.md
Multi-Level, Multi-Agent Timber Slab Design Method Proof of Concept
No full textThis repository contains the algorithmic methods and geometric results of the proof of concept for a multi-level, multi-agent timber slab design method. It includes the base plan, the structural simulation methods used for both its initial rough and subsequent fine grain analysis, the algorithm for its segmentation, and the two agent simulations used to first arrange, and then simplify, the slabs' internal structural reinforcement.
This design method is described in the related publication. To use this design method, first the 00_BaseFile must be opened with Rhino 3D. It can then be structurally analysed from within the grasshopper environment using 01_SofistikAnalsys. The result of the rough analysis, provided, can then be used for the segmentation of the slab within this same file. Once segmented, the slab can be analysed again. The result of the fine analysis, provided, can then be fed to 02_SplotchWeb_Generate to arrange the slab’s internal reinforcement
Replication Code for: Greedy Kernel Methods for Approximating Breakthrough Curves for Reactive Flow from 3D Porous Geometry Data
No full textThis dataset includes the code to reproduce the results from the paper titled "Greedy Kernel Methods for Approximating Breakthrough Curves for Reactive Flow from 3D Porous Geometry Data". In this paper we address the challenging application of 3D pore scale reactive flow under varying geometry parameters. We demonstrate that the vectorial kernel orthogonal greedy approximation (VKOGA) procedure with a data-adapted two-layer kernel yields excellent predictors for learning from 3D geometry voxel data via both morphological descriptors or principal component analysis. The numerical experiments from this paper can be reproduced using this code. See the README for more information and installation instructions
Replication Data for: "Numerical simulation of the Gross-Pitaevskii equation via vortex tracking"
No full textData for reproducibility of the numerical simulations of the research paper "Numerical simulation of the Gross-Pitaevskii equation via vortex tracking". The .zip file contains everything needed to generate the plots shown in the paper, as well as the code to run your own simulations of the Gross-Pitaevskii equation with the numerical method presented in the paper.
You just have to extract the tar file from this repository to access the files and the associated README.md