101 to 110 of 251 Results
Dec 15, 2023 - PN 2-3B
Santana Chacon, Pablo Filipe; Hammer, Maria; Wochner, Isabell; Walter, Johannes R.; Schmitt, Syn, 2023, "Replication Data for: A physiologically enhanced muscle spindle model: using a Hill-type model for extrafusal fibers as template for intrafusal fibers", https://doi.org/10.18419/DARUS-3796, DaRUS, V1
This code/data allows you reproduce the results of the paper: "A physiologically enhanced muscle spindle model: using a Hill-type model for extrafusal fibers as template for intrafusal fibers" by P. F. S. Chacon, M. Hammer, I. Wochner, J. R. Walter and S. Schmitt. Always cite the paper together with this dataset because this dataset is not self-exp... |
Dec 14, 2023 - Materials Design
Jung, Jong Hyun; Forslund, Axel; Srinivasan, Prashanth; Grabowski, Blazej, 2023, "Data for: Dynamically stabilized phases with full ab initio accuracy: Thermodynamics of Ti, Zr, Hf with a focus on the hcp-bcc transition", https://doi.org/10.18419/DARUS-3582, DaRUS, V1, UNF:6:PcXLVWUQ0T4geRQy0F0sgg== [fileUNF]
Data for the publication, Dynamically stabilized phases with full ab initio accuracy: Thermodynamics of Ti, Zr, Hf with a focus on the hcp-bcc transition, Phys. Rev. B 108, 184107 (2023). This data set contains 1) - the training sets (VASP files), - the low moment-tensor-potentials (MTPs) and high-MTPs (for seperate hcp and bcc phases and combined... |
Nov 30, 2023 - SciML PDE Benchmark
Takamoto, Makoto; Praditia, Timothy; Leiteritz, Raphael; MacKinlay, Dan; Alesiani, Francesco; Pflüger, Dirk; Niepert, Mathias, 2022, "PDEBench Pretrained Models", https://doi.org/10.18419/DARUS-2987, DaRUS, V2
This dataset contains the pretrained baseline models, namely FNO, U-Net, and PINN. These models are trained on different PDEs, such as 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. In addition the dataset contains the pre-train... |
Nov 20, 2023 - SPP2311: Ultrasound Neuromodulation
Werneck, Linda; Yildiz, Erdost; Han, Mertcan; Keip, Marc-Andre; Sitti, Metin; Ortiz, Michael, 2023, "Ion Flow Through Neural Ion Membrane: scripts and data", https://doi.org/10.18419/DARUS-3575, DaRUS, V1
The scripts and data are related to the numerical implementation of a quantitative model for ion flow through neural ion channels and a validation of the underlying single ion channel flow model for gramicidin A channels. The model is based on the Poisson-Nernst-Planck (PNP) equations for ion transport and is described in the related publication in... |
Nov 17, 2023 - PN 7-6
Kneifl, Jonas; Fehr, Jörg, 2023, "Crash Simulations of a Racing Kart's Structural Frame Colliding against a Rigid Wall", https://doi.org/10.18419/DARUS-3789, DaRUS, V1
Crash Simulations of a Racing Kart Frame Model This dataset contains results for several crash simulations of the frame of a racing kart colliding against a rigid wall. The wall and the frame itself are modeled as finite element models, implemented in the commercial software tool LS-Dyna. The latter comprises 9314 nodes, each possessing 3 translati... |
Nov 17, 2023 - DuMux
Buntic, Ivan; Coltman, Edward; Flemisch, Bernd; Ghosh, Tufan; Gläser, Dennis; Grüninger, Christoph; Hommel, Johannes; Keim, Leon; Kelm, Mathis; Koch, Timo; Kostelecky, Anna Mareike; Lipp, Melanie; Oukili, Hamza; Schneider, Martin; Utz, Martin; Wang, Yue; Weishaupt, Kilian; Wendel, Kai; Winter, Roman; Wu, Hanchuan, 2023, "DuMux 3.8.0", https://doi.org/10.18419/DARUS-3788, DaRUS, V1
Release 3.8.0 of DuMux, DUNE for Multi-{Phase, Component, Scale, Physics, ...} flow and transport in porous media. DuMux is a free and open-source simulator for flow and transport processes in and around porous media. It is based on the Distributed and Unified Numerics Environment DUNE. |
Potyka, Johanna; Schulte, Kathrin, 2023, "Setups for and Outcomes of Immiscible Liquid Droplet Collision Simulations", https://doi.org/10.18419/DARUS-3557, DaRUS, V3, UNF:6:qeJBCuC11BIa6WGzldHxhw== [fileUNF]
Tabulated data of the setups and outcomes of the immiscible liquid and single liquid droplet collisions for the publication Johanna Potyka, Kathrin Schulte: A volume of fluid method for three dimensional direct numerical simulations of immiscible droplet collisions, International Journal of Multiphase Flow, Volume 170, 2024, 104654, https://doi.org... |
Desai, Ishaan; Scheurer, Erik; Bringedal, Carina; Uekermann, Benjamin, 2023, "Micro Manager Version v0.3.0", https://doi.org/10.18419/DARUS-3764, DaRUS, V1
The Micro Manager is a tool to facilitate solving two-scale (macro-micro) coupled problems using the coupling library preCICE. The compressed source files of this data set are only meant to archive the version v0.3.0. If you want to use the Micro Manager, please follow the information on the preCICE website. This version of the Micro Manager is com... |
Oct 19, 2023 - SFB 1333 A3 - Schlaich group, ICP
Yang, Jie; Kondrat, Svyatoslav; Lian, Cheng; Liu, Honglai; Schlaich, Alexander; Holm, Christian, 2023, "Replication Data for: Solvent Effects on Structure and Screening in Confined Electrolytes", https://doi.org/10.18419/DARUS-3743, DaRUS, V1, UNF:6:KtlgApor9/WXUtTKp63TBw== [fileUNF]
This is the repository holding the data and python scripts we used for creating the corresponding figures in the publication. Tabular files include the ion and solvent (for solvent-explicit simulations) densiies for a hard-sphere primitive electrolyte model confined between two charged hard walls determined via classical density functional theory l... |
Sep 6, 2023 - Surrogate models for groundwater flow simulations
Pelzer, Julia, 2023, "Dataset: Single Heat Pump Simulation - Prepared "Learn Params", 100 Data Points", https://doi.org/10.18419/DARUS-3653, DaRUS, V1
This data set serves as training data for modelling the temperature field emanating from a groundwater heat pump. It is simulated with Pflotran, preprocessed with python and saved in pt-format. It contains 100 data points, each consisting of one simulation run after 5 years - so not necessarily in steady state. Each datapoint measures 100 m x 1280... |
