51 to 60 of 213 Results
Mar 28, 2024 - PN 7-6
Bechler, Florian, 2024, "Simulation Results from the Descriptive Graph-based Model of two Example Driving Scenarios", https://doi.org/10.18419/DARUS-4116, DaRUS, V1
Video of the interior and exterior information of a driving scenario, with the resulting graph-based description. The video shows a combination of exterior data of a driving scenario in combination with information about the drivers eyegaze. On the right side a resulting graph-based model is depicted which combines the states that are relevant for... |
Mar 27, 2024 - Publication Tools
Roy, Sarbani; Wang, Fangfang; Gläser, Dennis, 2024, "Harvester-Curator, a tool to elevate metadata provision in data and/or software repositories", https://doi.org/10.18419/DARUS-3785, DaRUS, V1
Harvester-Curator is a tool, designed to elevate metadata provision in data repositories. In the first phase, Harvester-Curator acts as a scanner, navigating through user code and/or data repositories to identify suitable parsers for different file types. It collects metadata from each of the files by applying corresponding parsers and then compile... |
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... |
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 7, 2024 - Usability and Sustainability of Simulation Software
Kschidock, Helena, 2024, "Replication Data for: Development of a framework for point-particle tracking", https://doi.org/10.18419/DARUS-3760, DaRUS, V1
This dataset contains a custom build for Altair's simulation software ultraFluidX and the input files used in the Master Thesis 'Development of an Euler-Lagrangian framework for point-particle tracking to enable efficient multiscale simulations of complex flows', submitted by Helena Kschidock on 02/11/2023. For further details on the input files, a... |
Feb 26, 2024 - Modelling, Simulation and Optimization for Agonist-Antagonist Myoneural Interface Surgeries
Homs Pons, Carme; Lautenschlager, Robin, 2024, "Replication Data for: Coupled Simulations and Parameter Inversion for Neural System and Electrophysiological Muscle Models", https://doi.org/10.18419/DARUS-4031, DaRUS, V1
This dataset allows to reproduce the results from the paper "Coupled Simulations and Parameter Inversion for Neural System and Electrophysiological Muscle Models" submitted to GAMM Mitteilungen in September 2023. Find more information about the structure of the dataset and the steps to reproduce the results in the readme.md file. |
Feb 16, 2024 - PN3-5
Sriram, Siddharth, 2024, "Data-driven analysis of structural instabilities in electroactive polymer bilayers based on a variational saddle-point principle: Datasets and ML codes", https://doi.org/10.18419/DARUS-3881, DaRUS, V1
The datasets and codes provided here are associated with our article entitled "Data-driven analysis of structural instabilities in electroactive polymer bilayers based on a variational saddle-point principle". The main idea of the work is to develop surrogate models using the concepts of machine learning (ML) to predict the onset of wrinkling insta... |
Feb 13, 2024 - SciML PDE Benchmark
Takamoto, Makoto; Praditia, Timothy; Leiteritz, Raphael; MacKinlay, Dan; Alesiani, Francesco; Pflüger, Dirk; Niepert, Mathias, 2022, "PDEBench Datasets", https://doi.org/10.18419/DARUS-2986, DaRUS, V8
This dataset contains benchmark data, generated with numerical simulation based on different PDEs, namely 1D advection, 1D Burgers', 1D and 2D diffusion-reaction, 1D diffusion-sorption, 1D, 2D, and 3D compressible Navier-Stokes, 2D Darcy flow, and 2D shallow water equation. This dataset is intended to progress the scientific ML research area. In ge... |
Jan 11, 2024 - Projects without PN Affiliation
Magiera, Jim M., 2024, "Replication Data for: Constraint-aware neural networks for Riemann problems", https://doi.org/10.18419/DARUS-3869, DaRUS, V1
Data sets of the article "Constraint-aware neural networks for Riemann problems", consisting of training and test data sets for Riemann solutions of the cubic flux model, an isothermal two-phase model, and the Euler equations for an ideal gas. You can find detailed information in the README.md. |
