1 to 10 of 80 Results
Jun 7, 2024 - ABxM Framework for Agent-based Modeling and Simulation
Nguyen, Long; Schwinn, Tobias; Groenewolt, Abel; Maierhofer, Mathias; Zorn, Max Benjamin; Stieler, David; Siriwardena, Lasath; Kannenberg, Fabian; Menges, Achim, 2022, "ABxM.Core: The Core Libraries of the ABxM Framework", https://doi.org/10.18419/darus-2994, DaRUS, V2
The ABxM.Core consists of the agent core library ABxM.Core and an interoperability library for Rhino 6 and later versions. ABxM.Core implements the functionality specific to agent-based modelling and simulation. The core library can, in principle, be referenced from any applicati... |
Jun 3, 2024 - Usability and Sustainability of Simulation Software
Chen, Jun; Chourdakis, Gerasimos; Desai, Ishaan; Homs-Pons, Carme; Rodenberg, Benjamin; Schneider, David; Simonis, Frédéric; Uekermann, Benjamin; Davis, Kyle; Jaust, Alexander; Kelm, Mathis; Kotarsky, Niklas; Kschidock, Helena; Mishra, Durganshu; Mühlhäußer, Markus; Schrader, Timo Pierre; Schulte, Miriam; Seitz, Valentin; Signorelli, Joseph; van Zwieten, Gertjan; Vinnitchenko, Niklas; Vladimirova, Tina; Willeke, Leonard; Zonta, Elia, 2024, "preCICE Distribution Version v2404.0", https://doi.org/10.18419/darus-4167, DaRUS, V1
The preCICE distribution is the larger ecosystem around preCICE, which includes the core library, language bindings, adapters for popular solvers, tutorials, and vagrant files to prepare a virtual machine image. The compressed source files of this data set are only meant to archi... |
May 27, 2024 - PN 6-4
Munz-Körner, Tanja; Weiskopf, Daniel, 2024, "Supplemental Material for "Exploring Visual Quality of Multidimensional Time Series Projections"", https://doi.org/10.18419/darus-3965, DaRUS, V1
Supplemental material for our paper "Exploring visual quality of multidimensional time series projections": A video demonstrating the interactive use of our exploration system. A table containing publications using dimensionality reduction on multidimensional time series to proje... |
May 15, 2024 - C03: Modelling of material injection processes into porous structures applied to vertebroplasty
Trivedi, Zubin; Wychowaniec, Jacek K.; Gehweiler, Dominic; Sprecher, Christoph Martin; Boger, Andreas; Gueorguiev, Boyko; D'Este, Matteo; Ricken, Tim; Röhrle, Oliver, 2024, "Data for: Rheological Analysis and Evaluation of Measurement Techniques for the Curing Polymethylmethacrylate Bone Cement in Vertebroplasty", https://doi.org/10.18419/darus-4004, DaRUS, V1, UNF:6:qWipgRgsaAlTQZDkmPv0LQ== [fileUNF]
This dataset includes the measurement data described in the paper "Rheological Analysis and Evaluation of Measurement Techniques for the Curing Polymethylmethacrylate Bone Cement in Vertebroplasty" and additional supplementary data. All data in .csv files except 'Inj1 -- 5' are g... |
May 3, 2024 - Supplementary Data: GALÆXI Paper
Kurz, Marius; Kempf, Daniel; Blind, Marcel P.; Kopper, Patrick; Offenhäuser, Philipp; Schwarz, Anna; Starr, Spencer; Keim, Jens; Beck, Andrea, 2024, "GALÆXI Verification: Convergence Tests", https://doi.org/10.18419/darus-4155, DaRUS, V1, UNF:6:vL1WFzY+Nmd1P5y9jFLWLg== [fileUNF]
This Dataset contains the setup and the results of the convergence tests which are reported in the GALÆXI Paper (Section 5.1). The results are contained in the file results.txt. The used case is based on the method of manufactured solution. The detailed formulation is specified i... |
May 3, 2024 - Supplementary Data: GALÆXI Paper
Kurz, Marius; Kempf, Daniel; Blind, Marcel P.; Kopper, Patrick; Offenhäuser, Philipp; Schwarz, Anna; Starr, Spencer; Keim, Jens; Beck, Andrea, 2024, "GALÆXI Validation: Taylor-Green Vortex", https://doi.org/10.18419/darus-4139, DaRUS, V1, UNF:6:Q71FGjHTEHnKVAq/VkFpXA== [fileUNF]
This Dataset contains the test case definition and reference data for the Taylor-Green Vortex (TGV) test case which builds the validation test case in the GALÆXI Paper (Section 5). Incompressible TGV (Ma=0.1) according to (Link): J. DeBonis, Solutions of the Taylor–Green vortex p... |
May 3, 2024 - Supplementary Data: GALÆXI Paper
Kurz, Marius; Kempf, Daniel; Blind, Marcel P.; Kopper, Patrick; Offenhäuser, Philipp; Schwarz, Anna; Starr, Spencer; Keim, Jens; Beck, Andrea, 2024, "GALÆXI Scaling", https://doi.org/10.18419/darus-4140, DaRUS, V1, UNF:6:q8ZbcGhBxtm6VlmuTlqskA== [fileUNF]
This Dataset contains the results of the scaling test cases of the GALÆXI Paper (Section 4). The scaling behavior is evaluated using simulations with resolutions ranging from 16384 up to 8.6x109 degrees of freedom using up to 1024 GPUs on JUWELS booster. The provided results cont... |
May 3, 2024 - Supplementary Data: GALÆXI Paper
Kurz, Marius; Kempf, Daniel; Blind, Marcel P.; Kopper, Patrick; Offenhäuser, Philipp; Schwarz, Anna; Starr, Spencer; Keim, Jens; Beck, Andrea, 2024, "GALÆXI Application: NASA Rotor 37", https://doi.org/10.18419/darus-4138, DaRUS, V1
This Dataset contains the setup data for the NASA Rotor 37 test case which corresponds to the application section in the GALÆXI Paper (Section 6). Reference (Link): L. Reid, R. D. Moore, Design and overall performance of four highly loaded, high speed inlet stages for an advanced... |
Apr 18, 2024 - Publication: Microfluidic experiments
Karadimitriou, Nikolaos; Lee, Dongwon; Vahid Dastjerdi, Samaneh; Steeb, Holger, 2024, "Primary drainage experiments and fractal dimensions", https://doi.org/10.18419/darus-4114, DaRUS, V1
The current repository contains raw data in the shape of images collected during a systematic laboratory study, examining the usability of the fractal dimension in the characterization of the flow regime. The study is presented in the paper by Karadimitriou et al., 2024. The two... |
Apr 11, 2024 - Holm group
Finkbeiner, Jan; Tovey, Samuel; Holm, Christian, 2024, "Replication Data for: Generating Minimal Training Sets for Machine Learned Potentials", https://doi.org/10.18419/darus-4099, DaRUS, V1
Data and scripts for replicating results and the investigation presented in the paper. This includes the dft parameters for generating training data, all training and data selection scripts for the neural networks, scripts for running and analysing the production simulations with... |
