121 to 130 of 213 Results
Jan 17, 2023 - PN 7-6
Rodegast, Philipp; Maier, Steffen; Kneifl, Jonas; Fehr, Jörg, 2023, "Simulation Data from Motorcycle Sensors in Operational and Crash Scenarios", https://doi.org/10.18419/DARUS-3301, DaRUS, V1, UNF:6:rnJlYpzgwi2nYAtNG7jBtA== [fileUNF]
This dataset provides time-dependent simulation results from high-fidelity motorcycle body crash scenarios. The set contains the angular as well as linear positions, velocities, and accelerations of different parts of the motorcycle. In addition, force and contact sensor signals are also part of the dataset. The driving scenarios include critical,... |
Jan 11, 2023 - Materials Design
Jung, Jong Hyun; Srinivasan, Prashanth; Forslund, Axel; Grabowski, Blazej, 2023, "Data for: High-accuracy thermodynamic properties to the melting point from ab initio calculations aided by machine-learning potentials", https://doi.org/10.18419/DARUS-3239, DaRUS, V1
Data for the publication High-accuracy thermodynamic properties to the melting point from ab initio calculations aided by machine-learning potentials, npj Comput. Mater., DOI: 10.1038/s41524-022-00956-8 (2023) This data set contains - the training sets (VASP files), - the low moment-tensor-potentials (MTPs) and high-MTPs, - the effective quasiharmo... |
Dec 16, 2022 - demoa
Wochner, Isabell; Schmitt, Syn, 2022, "MPC/OC Code for: Learning with Muscles: Benefits for Data-Efficiency and Robustness in Anthropomorphic Tasks", https://doi.org/10.18419/DARUS-3268, DaRUS, V1
This code allows you reproduce the optimal control and model predictive control results of the paper: "Learning with Muscles: Benefits for Data-Efficiency and Robustness in Anthropomorphic Tasks" by Isabell Wochner, Pierre Schumacher, Georg Martius, Dieter Büchler, Syn Schmitt and Daniel F.B. Haeufle. Always cite the paper together with this datase... |
Nov 2, 2022 - PN 5-6
Praditia, Timothy; Karlbauer, Matthias; Otte, Sebastian; Oladyshkin, Sergey; Butz, Martin V.; Nowak, Wolfgang, 2022, "Replication Data for: Learning Groundwater Contaminant Diffusion-Sorption Processes with a Finite Volume Neural Network", https://doi.org/10.18419/DARUS-3249, DaRUS, V1
This dataset contains diffusion-sorption data, generated with numerical simulation based on three different sorption isotherms, namely the linear, Freundlich, and Langmuir isotherms. This dataset is used to train, validate, and test all the deep learning models that are used in the publication "Learning Groundwater Contaminant Diffusion-Sorption Pr... |
Oct 28, 2022 - FAIR Fluids
Gültig, Matthias; Range, Jan Peter; Schmitz, Benjamin; Pleiss, Jürgen, 2022, "Self-diffusion coefficients of simulated aqueous glycerol mixtures", https://doi.org/10.18419/DARUS-3115, DaRUS, V1
In order to make thermophysical properties of complex liquid mixtures available to a comprehensive analysis, we developed a data management and analysis platform based on the standard data exchange format ThermoML. The practicability of integrating thermophysical data from experiment and simulation was demonstrated for two binary mixtures, methanol... |
Oct 28, 2022 - FAIR Fluids
Gültig, Matthias; Range, Jan Peter; Schmitz, Benjamin; Pleiss, Jürgen, 2022, "Densities of simulated aqueous methanol mixtures", https://doi.org/10.18419/DARUS-3112, DaRUS, V1
In order to make thermophysical properties of complex liquid mixtures available to a comprehensive analysis, we developed a data management and analysis platform based on the standard data exchange format ThermoML. The practicability of integrating thermophysical data from experiment and simulation was demonstrated for two binary mixtures, methanol... |
Oct 28, 2022 - FAIR Fluids
Gültig, Matthias; Range, Jan Peter; Schmitz, Benjamin; Pleiss, Jürgen, 2022, "Self-diffusion coefficients of simulated aqueous methanol mixtures", https://doi.org/10.18419/DARUS-3114, DaRUS, V1
In order to make thermophysical properties of complex liquid mixtures available to a comprehensive analysis, we developed a data management and analysis platform based on the standard data exchange format ThermoML. The practicability of integrating thermophysical data from experiment and simulation was demonstrated for two binary mixtures, methanol... |
Oct 28, 2022 - FAIR Fluids
Gültig, Matthias; Range, Jan Peter; Schmitz, Benjamin; Pleiss, Jürgen, 2022, "Densities of experimental aqueous methanol mixtures", https://doi.org/10.18419/DARUS-3116, DaRUS, V1
In order to make thermophysical properties of complex liquid mixtures available to a comprehensive analysis, we developed a data management and analysis platform based on the standard data exchange format ThermoML. The practicability of integrating thermophysical data from experiment and simulation was demonstrated for two binary mixtures, methanol... |
Oct 28, 2022 - FAIR Fluids
Gültig, Matthias; Range, Jan Peter; Schmitz, Benjamin; Pleiss, Jürgen, 2022, "Densities of experimental aqueous glycerol mixtures", https://doi.org/10.18419/DARUS-3117, DaRUS, V1
In order to make thermophysical properties of complex liquid mixtures available to a comprehensive analysis, we developed a data management and analysis platform based on the standard data exchange format ThermoML. The practicability of integrating thermophysical data from experiment and simulation was demonstrated for two binary mixtures, methanol... |
Oct 28, 2022 - FAIR Fluids
Gültig, Matthias; Range, Jan Peter; Schmitz, Benjamin; Pleiss, Jürgen, 2022, "Tracer-diffusion coefficients of experimental aqueous methanol mixtures", https://doi.org/10.18419/DARUS-3118, DaRUS, V1
In order to make thermophysical properties of complex liquid mixtures available to a comprehensive analysis, we developed a data management and analysis platform based on the standard data exchange format ThermoML. The practicability of integrating thermophysical data from experiment and simulation was demonstrated for two binary mixtures, methanol... |
