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Netgen/NGSolve code for pore-resolved simulations for: "Local Thermal Non-Equilibrium Models in Porous Media: A Comparative Study of Conduction effects"
No full textThis dataset contains the source code for the pore-resolved simulations presented in Kostelecky et al., Local Thermal Non-Equilibrium Models in Porous Media: A Comparative Study of Conduction effects, International Journal of Heat and Mass Transfer. TODO: add doi after acceptance.
The code generates a mesh of the fluid and solid geometry while resolving all internal interfaces, and solves a coupled heat conductive problem on the fluid and solid domains.</p
Supplemental Material for: mint: Integrating Scientific Visualizations Into Virtual Reality
No full textSupplemental Material for "mint: Integrating Scientific Visualizations Into Virtual Reality"
This repository contains the following directories and data:
evaluation/ - Raw data files resulting from mint frame lag performance measurements and Jupyter notebooks evaluating the data, generating the plots from the paper.
benchmarks/ - Python benchmark scripts used to run the VRAUKE/mint/MegaMol applications to gather performance measurements.
benchmarks/megamol/ - MegaMol project files used to run the VRAUKE/MegaMol real-world data set performance measurements.
screenshots/ - Screenshots of the VRAUKE application, showing the virtual reality scene and interaction tools in more detail, and the MegaMol benchmark data sets.
source-code/ - Source code excerpts from the mint library and VRAUKE Unity application, as well as source code excerpts from the mint integrations for MegaMol, Inviwo and ParaView. Note that the source code has been stripped for build dependencies and is not immediately ready for compilation. Rather, it is supposed to serve as reference.
mint-VRAUKE-VR-Demo-Video.mp4 - Video of a VRAUKE VR session with different visualization backends in use.
Appendix.pdf - An appendix to the paper containing additional measurements and detailed reporting regarding the MegaMol, Inviwo and ParaView mint API integrations.
Note that the software source code provided in the 'source-code/' directory is provided under individual licenses according to the respective software projects, which differ from the CC-BY-4.0 license that covers the rest of this data release
Replication Data for: Increasing Dynamic Accuracy using Predictive Feedforward with Hybrid Modeling
No full textExperimental dataset for model identification and validation of feedforward control on a five-axis milling machine.
This dataset belongs to the Open Access publication "Increasing dynamic accuracy of machine tools using predictive feedforward optimization with hybrid modeling" (doi: 10.1016/j.rcim.2025.103137) A detailed description of the setup can be found in the publication.
Feedback controllers from frequency inverter:
PI velocity controller with Kp = 0.035 [Nm/(rad/s)], Tn = 2.6 [ms].
P position controller with Kp = 35 [1/s].
The dataset contains the following folders (Feel free to contact the dataset owner if you have any questions about the dataset.):
Identification
measurements for identification of discrepancy model with different constant velocities as reference, and velocity sweeps for identification in frequency domain.
Validation\energy_consumption
measured energy consumption on the test bench of different feedforward schemes, quantified by direct current (DC) power
Validation\multi-axis butterfly contour
multi-axis tracking experiments comparing different feedforward schemes, the used butterfly G-code is also provided
Validation\sensitivity analysis
sensitivity analysis of the proposed MPFFC scheme against parametric model uncertainty
Validation\tracking single axis (const velocity)
single-axis tracking experiments at (unseen) consant velocities
Validation\tracking single axis (transient)
single-axis tracking experiments demonstrating transient behavior
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Replication Data for: Optimal information injection and transfer mechanisms for active matter reservoir computing (Gaimann and Klopotek, 2025)
No full text
This repository contains raw and post-processed replication data for the publication "Optimal information injection and transfer mechanisms for active matter reservoir computing" (Gaimann and Klopotek, 2025).
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.
We provide replication data for all figures and supplementary videos shown in our publication:
speed controller, with a linearly attractive driver
speed controller, with a linearly attractive driver, and a driver interaction strength of 2.0
speed controller, with a linearly attractive driver, and a Ridge parameter of 200.0
speed controller, with an inversely attractive driver
speed controller, with an inversely attractive driver, and a driver interaction strength of 2.0
speed controller, with an inversely attractive driver, and a Ridge parameter of 200.0
speed controller, with a repulsive driver (reproduction)
driver repulsion, with speed-controller setting of Lymburn et al. (2021)
driver repulsion, with speed-controller setting of Lymburn et al. (2021), and recorded kernel observations
driver repulsion, with speed-controller setting of Lymburn et al. (2021), with simulation box size 32.0 and observation box size 16.0
driver repulsion, with speed-controller setting of Lymburn et al. (2021), with simulation box size 32.0 and observation box size 32.0
driver repulsion, with speed-controller setting of Lymburn et al. (2021), with simulation box size 64.0 and observation box size 32.0
driver repulsion, with speed-controller setting of Lymburn et al. (2021), with a single agent
driver repulsion, with near-critically damped speed-controller setting
driver repulsion, with near-critically damped speed-controller setting, with a single agent
driver attraction (inverse)
driver attraction (inverse), with a single agent (near-critically damped speed-controller setting)
agent-agent repulsion, with a repulsive driver
agent-agent repulsion, with an (inversely) attractive driver
agent-agent repulsion, with an (inversely) attractive driver, and a driver interaction radius of 2.0
agent-agent repulsion, with an (inversely) attractive driver, and a Lorenz-96 driving protocol
agent-agent repulsion vs. number of agents, with a repulsive driver
agent-agent repulsion vs. number of agents, with an (inversely) attractive driver
agent-agent repulsion vs. number of agents, with an (inversely) attractive driver, an agent-agent repulsion radius of 1.0, and a driver interaction strength of 100.0
agent-agent repulsion vs. number of agents, with an (inversely) attractive driver, an agent-agent repulsion radius of 1.0, and a driver interaction strength of 11.2883789
Ridge parameter vs. target agent speed, with a linearly attractive driver
short-range agent-agent repulsion, with an agent-agent repulsion radius of 1.0
short-range agent-agent repulsion, with an agent-agent repulsion radius of 4.0
long-range agent-agent repulsion, with an agent-agent repulsion radius of 1.0
long-range agent-agent repulsion, with an agent-agent repulsion radius of 4.0
viscoelastic fluid, with a repulsive driver and a driver interaction strength of 100.0
viscoelastic fluid, with a repulsive driver and a driver interaction strength of 1000.0
undriven swarm, with a near-critically damped speed-controller setting
We note that the controlled variable (config setting) "interaction_types__driver_attraction" adds a linear (homing-style) driver attraction interaction, while the variable "interaction_types__driver_repulsion" combined with a negative value for "interaction_types__driver_repulsion__strength" adds an inversely driver attraction interaction.
By default, we use a Lorenz-63 driving protocol that was generated on the fly during the simulation. One scan uses this Lorenz-96 driving protocol.
Each dataset typically contains 400 parameter combinations. Each parameter combination contains four files:
config.yaml: controlled variables
simulation_output_train.h5: physical simulation observables in first (training) run
simulation_output_test.h5: physical simulation observables in second (testing) run
reservoir_computer_output.h5: observables related to reservoir computing and time series prediction
The second run has a different chaotic driving protocol, using the same underlying dynamical system (chaotic attractor) but different initial conditions. By default, for all driven simulations, physical observables are only recorded for the test run for a full reservoir computing train/test cycle. Each simulation typically consists of 1,000.00 time units (50,000 integration time steps of 0.02 time units by default). A burn-in phase of 20.0 simulation time units (1,000 integration time steps of 0.02 time units by default) takes place at the beginning of each simulation, which is not recorded. Controlled variables are stored as HDF5 attributes. At each step, we predict by default 25 integration time steps ahead (=0.45283 L63-Lyapunov times).
The simulation output files contain:
agent_observables: positions, velocities, total forces, velocity fluctuations for all agents; for the first 20.0 simulation time units
frame_observables: driver position (external driving trajectory / input time series), center of mass (taking periodic boundary conditions into account), agent-averaged observables, scalar polarity, scalar rotation; for the full simulation
histograms: binned agent observables and derived quantities; for the full simulation
radially_binned: radial distribution function (agent count), connected velocity correlation, cumulative velocity correlation
time_lags: auto-correlations of agent observables and derived quantities, two-time correlations of agent observables and derived quantities
reference_frame_steps: reference frames (measured in integration steps) for the recording of delay-based quantities in time_lags
The reservoir computer output files contain:
linear_regression_model: the weights of the linear model (readout layer)
observer_kernel_params: placement positions and widths of the Gaussian observation kernels
predictions_train: n-steps-ahead prediction using the trained linear model, on training data
predictions_test: n-steps-ahead prediction using the trained linear model, on testing data
Aggregates of physical observables across all parameter combinations in a single dataset are stored as CSV files for convenience; the relevant observable is indicated by the file name. Files that carry the "time_avg" tag are averaged over all simulation time steps, for the "ensemble_avg" averaged over all seeds (only one seed is used here), and for the "array_avg" averaged over all recorded entries (typically samples at different time steps). We provide the following aggregated observables that were processed to generate figures in our associated publication:
lymburn_correlation_coefficient: Correlation coefficient, predictive performance
agent_avg_msd_at_lyapunov_time_step=55: Agent-averaged mean squared displacement at the Lyapunov integration time step of the Lorenz-63 attractor (after 55 integration time steps of 0.02 each)
first_local_min.array_avg.h5?connected_velocity_correlation: First local minimum of the connected velocity correlation function, averaged over all recorded samples
ensemble_avg.array_avg.n_activated_kernels_threshold=0.001: Time-averaged number of activated observation kernels for agent count, firing at least above a threshold value of 0.001
ensemble_avg.array_avg.smallest_agent_distance_circle_around_driver: Time-averaged and agent-averaged circle area spanned by the distance between an agent i and its next-nearest neighboring agent j
mean_speed: Agent-averaged speed
scalar_polarity: Scalar polarity
scalar_rotation: Scalar rotation
attanasi_susceptibility: Dynamical susceptibility
The supplementary videos generated using this raw data are published as: Gaimann, M. U., & Klopotek, M. (2025). Supplementary Videos for: Optimal information injection and transfer mechanisms for active matter reservoir computing (Gaimann and Klopotek, 2025). DaRUS. doi:10.18419/DARUS-4806
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Replication Data for: Solving Approximation Tasks with Greedy Deep Kernel Methods
No full textThis archive contains the Python code and result files corresponding to the paper "Solving Approximation Tasks with Greedy Deep Kernel Methods" and allows to reproduce all numerical experiments
Data repository for "Loss Behavior in Supervised Learning With Entangled States"
No full text
Replication code and experiment result data for training Parameterized Quantum Circuits (PQCs) with entangled data. The experiments evaluate the structure of the loss landscape during training based on the training sample that is used for training.
The combined experiment and data extraction scripts are contained in
experiments_and_data_extraction.zip. The experiment results that are used in the related publication are contained in raw_results.zip.
Experiment/Reproduction setup (experiments_and_data_extraction.zip):
env.sh: Contains specification of experiment environment. Adjust the number of available CPUs as needed.
run_exp_5.py: Entrypoint for experiments for training 5-qubit PQCs. The used PQCs and hyperparameters can be adjusted in this file.
data_extraction.py: Extraction and aggregation of experiment results in preparation for the contained plots. Note that this step was already executed on the results in raw_results.zip and can be omitted if the plots should be reconstructed using the available data.
states_3_qubits.py and states_5.npy contain input states used for the experiments (sorted by number of qubits).
Plots:
improvement_neighborhood.pdf: Comparison of the improvement in a neighborhood ordered by the Schmidt rank of the input. Created by improvement_plot.py.
improvement_by_schmidt_rank.pdf and improvement_by_entanglement_entropy.pdf: Comparison of improvement order by entanglement measures. Created by improvement_by_entanglement.py.
expressibilities_separate.pdf: Comparison of expressivity by PQCs. Created by expressibility_plots.py.
distance_to_zero_comparison.pdf: Comparison of distance to minimum in loss landscape. Created by distance_to_zero_plot.py.
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Replication data of Sottmann group for: "In Situ Ultra-Small- and Small-Angle X-ray Scattering Study of ZnO Nanoparticle Formation and Growth through Chemical Bath Deposition in the Presence of Polyvinylpyrrolidone"
No full textAll processed data can be found here.The data is structured according to figures and schemes in the research article and contains the following data types: csv, tif (format
Service Execution Submodel for the Asset Adminstration Shell
No full textThis AAS submodel is a non-standardized academic proposal. The submodel template aims at the interoperable provision of metadata for orchestrating containerized services related to the asset of the respective Asset Administration Shell.
Please refer to README.md contained in the dataset for more detailed information
Supplementary Information to "Molecular determinants of solvent nanoseparation by nanoporous carbon materials"
No full textFiles and structures for performing and analysing coarse-grained molecular dynamics simulations of solvent diffusion through nanoporous carbon materials
Replication data for: Cyclopalladation of a Covalent Organic Framework for Near-Infrared Light-Driven Photocatalytic Hydrogen Peroxide Production
No full textAll primary and processed data of the journal article can be found here. The data is structured according to analytical techniques. The material measured is described in a file name. The dataset includes only experimental data and no simulational data. The measurement methods are described in the SI of the journal article. The data can be used to replicate the experiments, to evaluate and compare the materials' properties with others, and to investigate the materials' structures