PN 2

SimTech EXC 2075 Project Network 2 "In silico models of coupled biological systems"

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311 to 320 of 376 Results

MPC/OC Code for: Learning with Muscles: Benefits for Data-Efficiency and Robustness in Anthropomorphic Tasks 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...
mpc_oc_code_corl.tar.gz Dec 16, 2022 - MPC/OC Code for: Learning with Muscles: Benefits for Data-Efficiency and Robustness in Anthropomorphic Tasks Gzip Archive - 227.9 MB - MD5: 89792d633142a824f2a9e151d84d1594 Code for model predictive control (MPC) and optimal control (OC) to control musculoskeletal models in demoa. Please read ReadMe to find a description how to reproduce the numerical experiments of the paper.
README.md Dec 16, 2022 - MPC/OC Code for: Learning with Muscles: Benefits for Data-Efficiency and Robustness in Anthropomorphic Tasks Markdown Text - 4.7 KB - MD5: 5cbff34b49a706365c6874dc64fb1239 This file describes how to use and install the necessary code. Please read it to find out how to reproduce the experiments from the paper.
Self-diffusion coefficients of simulated aqueous glycerol mixtures 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...
sim_transp_glyc.xml Oct 28, 2022 - Self-diffusion coefficients of simulated aqueous glycerol mixtures XML - 46.9 KB - MD5: 36b6a6a26016a3252612e095702f0f4d
Densities of simulated aqueous methanol mixtures 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...
sim_dens_meth.xml Oct 28, 2022 - Densities of simulated aqueous methanol mixtures XML - 25.4 KB - MD5: e05ca93c7efe519df9e02cb2a752dff2
Self-diffusion coefficients of simulated aqueous methanol mixtures 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...
sim_transp_meth.xml Oct 28, 2022 - Self-diffusion coefficients of simulated aqueous methanol mixtures XML - 36.1 KB - MD5: e1483dc1b0b77b497b8f5a9338a81c70
Densities of experimental aqueous methanol mixtures 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...

MPC/OC Code for: Learning with Muscles: Benefits for Data-Efficiency and Robustness in Anthropomorphic Tasks

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...

mpc_oc_code_corl.tar.gz

Dec 16, 2022 - MPC/OC Code for: Learning with Muscles: Benefits for Data-Efficiency and Robustness in Anthropomorphic Tasks

Gzip Archive - 227.9 MB -

Code for model predictive control (MPC) and optimal control (OC) to control musculoskeletal models in demoa. Please read ReadMe to find a description how to reproduce the numerical experiments of the paper.

README.md

Dec 16, 2022 - MPC/OC Code for: Learning with Muscles: Benefits for Data-Efficiency and Robustness in Anthropomorphic Tasks

Markdown Text - 4.7 KB -

This file describes how to use and install the necessary code. Please read it to find out how to reproduce the experiments from the paper.

Self-diffusion coefficients of simulated aqueous glycerol mixtures

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...

sim_transp_glyc.xml

Oct 28, 2022 - Self-diffusion coefficients of simulated aqueous glycerol mixtures

XML - 46.9 KB -

Densities of simulated aqueous methanol mixtures

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

sim_dens_meth.xml

Oct 28, 2022 - Densities of simulated aqueous methanol mixtures

XML - 25.4 KB -

Self-diffusion coefficients of simulated aqueous methanol mixtures

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

sim_transp_meth.xml

Oct 28, 2022 - Self-diffusion coefficients of simulated aqueous methanol mixtures

XML - 36.1 KB -

Densities of experimental aqueous methanol mixtures

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

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