371 to 380 of 1,830 Results
Mar 25, 2024 - Geostatistical Inversion
Xu, Teng, 2024, "Benchmarking in subsurface hydrological inversion: high-fidelity reference solution and EnKF replication data", https://doi.org/10.18419/DARUS-2382, DaRUS, V1, UNF:6:vW5NY5+W7Wy1GtNndUOs3Q== [fileUNF]
Description: Dataset published with the paper "Towards a community-wide effort for benchmarking in subsurface hydrological inversion: benchmarking cases, high-fidelity reference solutions, procedure and a first comparison". You can use these data to generate the comparisons between the EnKF and the MCMC solution as seen in the paper. Folder structu... |
Mar 25, 2024 - A01: Molecular detail in fluid simulations: Density Functional Theory within component and momentum balances
Bursik, Benjamin; Stierle, Rolf; Schlaich, Alexander; Rehner, Philipp; Gross, Joachim, 2024, "Additional Material: Viscosities of Inhomogeneous Systems from Generalized Entropy Scaling", https://doi.org/10.18419/DARUS-3769, DaRUS, V1
This data set contains data of three categories: 1) LAMMPS input files (.lammps), postprocessing python script (.py) and density and velocity profiles (.dat) from NEMD. 2) DFT three-dimensional density profiles (.npy) for all systems. 3) Jupyter notebooks (.ipynb) for the calculation of densities from DFT, viscosity and velocity profiles from entro... |
Mar 22, 2024 - SFB-TRR 161 A07 "Visual Attention Modeling for Optimization of Information Visualizations"
Wang, Yao; Bulling, Andreas, 2024, "Saliency3D: A 3D Saliency Dataset Collected on Screen (Dataset and Experiment Application)", https://doi.org/10.18419/DARUS-4101, DaRUS, V1
While visual saliency has recently been studied in 3D, the experimental setup for collecting 3D saliency data can be expensive and cumbersome. To address this challenge, we propose a novel experimental design that utilizes an eye tracker on a screen to collect 3D saliency data. Our experimental design reduces the cost and complexity of 3D saliency... |
Mar 21, 2024 - 2019_DFG_ZRA_Gleichlaufkompensation
Steinle, Lukas, 2024, "Replication Data for: Learning Compensation of the State-Dependent Transmission Errors in Rack-and-Pinion Drives", https://doi.org/10.18419/DARUS-3759, DaRUS, V1, UNF:6:yyyPRR4Wz8WOv1lj5nOToA== [fileUNF]
This dataset contains all experimental data that is shown within the paper "Learning Compensation of the State-Dependent Transmission Errors in Rack-and-Pinion Drives". Rack-and-pinion drives are commonly used in large machine tools to provide linear motion of heavy loads over long travel distances. A key concern in this context is the achievable p... |
Mar 19, 2024 - Scientific Computing
Bantel, Linus; Domanski, Peter, 2024, "Quanser IP-02 Inverted Cartpole Measurement Data for Analysis and Offline RL", https://doi.org/10.18419/DARUS-4085, DaRUS, V1, UNF:6:8MdHTluRf6u9Vntb4fvs/g== [fileUNF]
Recorded Data of the Swingup Task on the Inverted Cartpole IP-02 by Quanser. State transitions are as following: (pos, pos_dot, phi, phi_dot, action) -> (pos_new, pos_dot_new, phi_new, phi_dot_new) Actions are discrete from 0 to 8, with 4 being the zero-action. I.e. 0 and 8 are full force in either direction. |
Mar 19, 2024 - Institute for Stochastics and Applications
Höpfl, Sebastian, 2024, "Percentile Intervals in Bayesian Inference are Overconfident", https://doi.org/10.18419/DARUS-4068, DaRUS, V1
This dataset demonstrates the difference in calculating percentile Intervals as approximation for Highest Density Intervals (HDI) vs. Highest Posterior Density (HPD). This is demonstrated with extended partial liver resection data (ZeLeR-study, ethical vote: 2018-1246-Material). The data includes Computed Tomography (CT) liver volume measurements o... |
Mar 15, 2024 - Ikarus
Müller, Alexander; Vinod Kumar Mitruka, Tarun Kumar Mitruka; Jakob, Henrik, 2024, "Ikarus v0.4", https://doi.org/10.18419/DARUS-3889, DaRUS, V1
Ikarus is a C++-based library with Python bindings (link to documentation) built to solve partial differential equations with the finite element method. The dataset under this DOI contains the current release (v0.4) of the library. This release not only focuses on refactoring various interfaces but also introduces exciting features such as Python b... |
Reiser, Philipp; Aguilar, Javier Enrique; Guthke, Anneli; Bürkner, Paul-Christian, 2024, "Replication Code for: Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference", https://doi.org/10.18419/DARUS-4093, DaRUS, V1
This code allows to replicate key experiments from our paper: Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference. For further details, please refer to the README.md. |
Alvarez Chaves, Manuel; Gupta, Hoshin; Ehret, Uwe; Guthke, Anneli, 2024, "Replication Data for: On the Accurate Estimation of Information-Theoretic Quantities from Multi-Dimensional Sample Data", https://doi.org/10.18419/DARUS-4087, DaRUS, V1
Non-Parametric Estimation in Information Theory 1. Introduction This is a repository for our paper on: "On the Accurate Estimation of Information-Theoretic Quantities from Multi-Dimensional Sample Data". The projects is organizes as follows: ├── analysis_results\
│ ├── plots\
├── data_evaluation\
│ ├── data\
│ ├── notebooks\
│ ├── results\... |
Mar 14, 2024 - HLRS-exaFOAM
Lesnik, Sergey; Rusche, Henrik, 2024, "exaFOAM Microbenchmark MB10 - ERCOFTAC conical diffuser LES", https://doi.org/10.18419/DARUS-3745, DaRUS, V1
This work is part of the exaFOAM project that aims to enable the open-source CFD software OpenFOAM to exploit massively parallel HPC architectures and overcome performance scaling bottlenecks. The ERCOFTAC conical diffuser is a challenging case for turbulence modeling since it operates in the flow regime close to separation at the diffuser wall. De... |
