DaRUS (University of Stuttgart)
Not a member yet
2037 research outputs found
Sort by
Dataset for: Nanoparticle-Polymer Coupling in Magnetic Gels Studied by Means of Computer Simulations and Experiments
No full textThis dataset contains the data in context with the named paper - Nanoparticle-Polymer Coupling in Magnetic Gels Studied by Means of Computer Simulations and Experiments - namely the raw data of the plots and the plot skripts to generate them.
The experimental data comprises AC magnetic susceptibility spectra of a PAAm hydrogel loaded with cobalt ferrite particles for varying degree of crosslinking and monomer fraction. Simulation data mainly include AC susceptibility spectra for different isotropic and anisotropic nanoparticle-polymer coupling and varying mesh size. For more information (normalization etc.), see the readme.md file
Figures of "Traversing Dual Realities: Investigating Techniques for Transitioning 3D Objects between Desktop and Augmented Reality Environments"
No full textHere we provide access to the images used in our paper "Traversing Dual Realities: Investigating Techniques for Transitioning 3D Objects between Desktop and Augmented Reality Environments".
The figures describe the proposed interaction techniques, a simplified transition pipeline, a schematic study setup, and show the results of the technique study.
By providing the figures here, they can be freely used with other materials but with the proper reference to the paper.
This work was funded by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy - EXC 2075 - 390740016
Deep Drawing and Cutting Simulations Dataset
No full textThe benchmark dataset was generated through a comprehensive simulation study of the deep drawing process for DP600 sheet metal, incorporating variations in geometry, material properties, and process parameters. The simulations were based on the deep drawing of modified quadratic cups with a length of 210 mm and a drawing depth of 30 mm. Three distinct base geometries - Concave, Convex, and Rectangular - were derived from a rectangular reference shape, with key geometric parameters varied in two increments (minimum and maximum).
For each geometry, material and process parameters such as the hardening factor (MAT), friction coefficient (FC), sheet thickness (SHTK), and binder force (BF) were systematically varied, resulting in 32,076 unique simulations. Each simulation included stress, strain, thickness distribution, and nodal displacement data for the deep drawing and subsequent springback analysis.
The simulation data were stored in HDF5 format, with metadata linking each dataset to its corresponding geometry, material, and process parameters. This structured format ensures efficient retrieval and processing of simulation results, facilitating further analysis and benchmarking
Software for "A generalized Riemann problem solver for a hyperbolic model governing two-layer thin film flow"
No full textIn this project, we developed a Generalized Riemann solver for a hyperbolic model which governs first order dynamics of two-layer thin film flow under the influence of an anti-surfactant. The solver is a one-dimensional spatial-temporal coupled second-order finite-volume solver and can be utilized for further development
Source code for: KaReMo++
No full textThis repository contains the python code for the generation of realistically scattering stiffnesses and strengths of softwood glulam beams. The model is an extension of the Karlsruher Rechenmodell. The KaReMo++ model was developed in the dissertation of Janusch Töpler
Code for Improving Video Caption Accuracy with LLMs
No full textAs part of the IKILeUS project at the University of Stuttgart, research was conducted to explore how Large Language Models (LLMs) can enhance the accuracy and contextual relevance of automatic speech recognition (ASR)-generated captions. While ASR tools provide a foundation for accessibility, they often produce grammatical errors, misinterpret homophones, and struggle with domain-specific terminology. To address these challenges, experiments were conducted using LLMs such as GPT-3.5 and Llama2-13B to refine and correct captioning errors. The models were evaluated using standard NLP metrics such as Word Error Rate (WER), BLEU, and ROUGE scores, demonstrating notable improvements in caption accuracy. The findings suggest that LLMs can effectively enhance the readability, coherence, and precision of automatically generated captions, offering a promising direction for improving video accessibility for the Deaf and Hard of Hearing (DHH) community
Replication Data for: Learning from Mixing Power Curves: An AI-based Approach for Online Assessment of Fresh Concrete Consistency
No full textOverview
This dataset contains a synthetic benchmark for studying data-driven assessment of fresh concrete consistency from mixer power consumption time series ("mixing curves"). It was created in the context of the manuscript Learning from Mixing Power Curves: An AI-based Approach for Online Assessment of Fresh Concrete Consistency (submitted to the Journal of Building Engineering).
The data links randomized concrete mix designs, simulated mixing power curves, and simulated slump flow values as a consistency measure. It is intended for developing, pretraining, and evaluating machine learning models that use process data from concrete mixing plants, with a focus on long short-term memory (LSTM) networks and other sequence models.
Data contents
All tabular data are provided in machine-readable formats (e.g. CSV) with one row per mix and clearly named columns. The dataset consists of:
Mix design parameters
Cement, filler, aggregate and water contents per m³, water-to-powder ratio, and related scalar descriptors used in the simulation.
Mixing power curves
Synthetic mixing curves with a fixed sequence length (90 time steps). Each curve represents the electrical power consumption of a single (hypothetical) mixer over one batch, simulated according to an empirically supported six-stage mixing model.
Reference slump flow values
Simulated slump flow for each mix, derived from empirical relationships between mix proportions, rheological parameters and slump flow reported in the literature.
Simulation framework
Mixing curves are generated using a six-stage concrete mixing kinetics model that captures key transitions such as granule formation, fluidity point and subsequent stabilization of power consumption. Slump flow labels are computed from the mix design and late-stage mixing power values using empirical relationships between mix proportions, rheological parameters, and slump flow.
The dataset therefore offers a controlled environment to test whether and how sequence models can learn to infer fresh concrete consistency from the temporal development of mixing power, beyond what can be obtained from mix proportions and single end-of-mixing power values.
Intended use
The dataset is designed for:
Pretraining and benchmarking sequence models (e.g. LSTM, GRU, transformers)
Comparing sequence-based and static models for consistency prediction
Method development for online quality assessment in concrete production
Teaching and demonstration of AI methods in civil / materials engineering
Variable Definition
torque_x: A series of 90 time-steps reflecting the mixer's power consumption from start until end of mixing. One value per time-step.
water, cement, filler, aggregate: (content of given mix constituents)
slump flow value: The slump flow value based on the mix proportion as well as the power consumption in mixing stage VI
Variable definitions, units and simulation parameters are documented in greater detail in the related publication.
Version history
2025-03: Initial upload.
2025-12: Updated version of the dataset including minor fixes and an updated terminology / naming convention across the board.
</ul
COLife_06: Gigamap, Synthesis Map, DIY, Project Portfolio and Introduction
No full textThe data cover a gigamap codesigned with the stakeholders and team members, a synthesis map, contemplating on the gigamap's data. It also covers instruction materials on how to reproduce the project itself. Portfolio and several instruction materials. The project focuses on a more-than-human perspective in a central urban environment. This time, COLife also focuses on reptiles amongst bats and birds and insects
arm26 Version for: Closed-Loop Coupling of Both Physiological Spindle Model and Spinal Pathways for Sensorimotor Control of Human Center-Out Reaching
No full textVersion of the arm26 model (https://doi.org/10.18419/DARUS-2871) used in the study 'Closed-loop coupling of both physiological spindle model and spinal pathways for sensorimotor control of human center-out reaching', including additional scripts to run with python, muscle spindle module and NEST simulator.
The file contains an archive including all relevant data to run the simulation in the simulator demoa. This needs to be installed separately and is available as open source too (get-demoa.com).
If you use this model, please cite the related publication together with this dataset
Supplementary Videos for: Optimal information injection and transfer mechanisms for active matter reservoir computing (Gaimann and Klopotek, 2025)
No full text
This dataset contains supplementary videos for the publication "Optimal information injection and transfer mechanisms for active matter reservoir computing" (Gaimann and Klopotek, 2025) (to be published).
The datasets contain physical observables recorded during non-equilibrium simulations of active matter systems (swarms) driven by an external force. These simulations serve as information processors in a reservoir computing setup.
The videos show active matter systems (swarms) driven by an external force. These swarm systems can be used to predict the future trajectory of the external driving force using reservoir computing. We use the chaotic attractor Lorenz-63 as the external driving protocol and as a benchmark. Agents are colored by their current speed. The driver is marked as a black spiked ball, follows a fixed trajectory specified by the driving protocol, and exerts a repulsive force on the agents. The past positions of agents and drivers in a time window of 0.1 time units (5 integration time steps of 0.02 time units as default) are displayed as traces. Agents experience local repulsion, global attraction (homing) to the center of the simulation box, speed control towards a constant agent speed, and local driver interaction. Specifically, in this work, we present simulations with two types of attractive drivers (linear and inverse). A sigmoid force clamp (wrapper) processes and limits the total force experienced by each agent. The simulation uses periodic boundary conditions. Velocity fluctuations are colored by their orientation; the green cross indicates the center of mass. By default, we use 200 agents.
Each video corresponds to a specific parameter combination or a point in a parameter scan presented in the corresponding publication, or to a specific parameter combination. We provide videos for the following parameter scans:
speed-controller scan, with inversely attractive driver
speed-controller scan, with inversely attractive driver (velocity fluctuations)
speed-controller scan, with linearly attractive driver
speed-controller scan, with linearly attractive driver (velocity fluctuations)
driver repulsion scan, near-critical speed-controller setting
driver repulsion scan, near-critical speed-controller setting, single agent
driver repulsion scan, Lymburn et al. (2021) speed-controller setting
inverse driver attraction scan, near-critical, single agent
agent-agent repulsion scan, with a repulsive driver
agent-agent repulsion scan, with an inversely attractive driver
inverse driver attraction scan, near-critical speed-controller setting
agent repulsion strength vs. number of agents scan, with a repulsive driver
agent repulsion strength vs. number of agents scan, with an inversely attractive driver
agent repulsion strength vs. number of agents scan, with an inversely attractive driver, with a repulsion radius of 1.0 and a driver strength of 100.0
agent repulsion strength vs. number of agents scan, with an inversely attractive driver, with a repulsion radius of 1.0 and a driver strength of 11.2883789
viscoelastic fluids
undriven system, near-critical speed-controller setting
The raw data used to generate these videos is published as: Gaimann, M. U., & Klopotek, M. (2025). Optimal information injection and transfer mechanisms for active matter reservoir computing (Gaimann and Klopotek, 2025). DaRUS. https://doi.org/10.18419/DARUS-4805.
</p