EXC 2075 Project Networks

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Author Affiliation: Universität Stuttgart Deposit Date: 2022

1 to 10 of 18 Results

Supporting Information: Chemical Potential Differences in Nanoscopic Teflon/Kapton Capillaries Jun 6, 2024 - PN 1-8 Stärk, Philipp; Schlaich, Alexander, 2024, "Supporting Information: Chemical Potential Differences in Nanoscopic Teflon/Kapton Capillaries", https://doi.org/10.18419/darus-3149, DaRUS, V1 This is the repository holding the supporting information for atomistic Molecular Dynamics Simulations of Teflon/Kapton capillaries. Here we list the simulation input scripts as well as analysis scripts. See the README file for more information.
Material for the paper "The possible role of lipid bilayer properties in the evolutionary disappearance of betaine lipids in seed plants." Jul 18, 2023 - BLinK Schlaich, Alexander, 2023, "Material for the paper "The possible role of lipid bilayer properties in the evolutionary disappearance of betaine lipids in seed plants."", https://doi.org/10.18419/darus-2360, DaRUS, V1 Simulation input scripts to produce the data presented in the manuscript "The possible role of lipid bilayer properties in the evolutionary disappearance of betaine lipids in seed plants." All simulations were carried out using the GROMACS simulation package. The folders contain...
Pre-trained and fine-tuned ANI models for: Transfer learning for chemically accurate interatomic neural network potentials Feb 20, 2023 - PN 6 Zaverkin, Viktor; Holzmüller, David; Bonfirraro, Luca; Kästner, Johannes, 2023, "Pre-trained and fine-tuned ANI models for: Transfer learning for chemically accurate interatomic neural network potentials", https://doi.org/10.18419/darus-3299, DaRUS, V1 Pre-trained and fine-tuned ANI models using the Gaussian Moments Neural Network (GM-NN) approach. Code for GM-NN implemented within the Tensorflow framework, including the respective documentation and tutorials, can be found on GitLab. The data represents TensorFlow v2 checkpoint...
Gaussian active learning on multi-resolution arbitrary polynomial chaos emulator Feb 15, 2023 - PN 5 Kohlhaas, Rebecca; Kröker, Ilja; Oladyshkin, Sergey; Nowak, Wolfgang, 2023, "Gaussian active learning on multi-resolution arbitrary polynomial chaos emulator", https://doi.org/10.18419/darus-2829, DaRUS, V1 This folder contains the code for the aMR-PC toolbox by Ilja Kröker in the version used for the code in GALMAP_code. This toolbox was also used for Kröker et al. 2022 Link to current version of the toolbox here Data This folder contains inputs and simulated outputs of the CO_2 be...
Code for: Seasonal Dynamics of Gaseous CO2 Concentrations in a Karst Cave Correspond With Aqueous Concentrations in a Stagnant Water Column Feb 13, 2023 - C04: Pore-scale and REV-scale approaches to biological and chemical pore-space alteration in porous media Keim, Leon; Class, Holger; Schirmer, Larissa; Strauch, Bettina; Wendel, Kai; Zimmer, Martin, 2023, "Code for: Seasonal Dynamics of Gaseous CO2 Concentrations in a Karst Cave Correspond With Aqueous Concentrations in a Stagnant Water Column", https://doi.org/10.18419/darus-3276, DaRUS, V1 This dataset contains the DuMux code for the simulations in https://doi.org/10.3390/geosciences13020051 For the detailed list of software used, please have a look at the file install_class2023.sh. To run the simulations at your own computer, please conduct the following steps: In...
Data for: Measurement Campaign of Gaseous CO2 Concentrations in a Karst Cave with Aqueous Concentrations in a Stagnant Water Column 2021-2022. Feb 13, 2023 - C04: Pore-scale and REV-scale approaches to biological and chemical pore-space alteration in porous media Keim, Leon; Class, Holger; Schirmer, Larissa; Wendel, Kai; Strauch, Bettina; Zimmer, Martin, 2023, "Data for: Measurement Campaign of Gaseous CO2 Concentrations in a Karst Cave with Aqueous Concentrations in a Stagnant Water Column 2021-2022.", https://doi.org/10.18419/darus-3271, DaRUS, V1 This dataset contains data generated during the measurement campaign inside the karst cave. The CO2 sensors in the cave air will continue to measure (as of Feb. 2023). For details on the site etc. see https://doi.org/10.3390/geosciences13020051 To create the graphs in the Class e...
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 an...
demoa-base: a biophysics simulator for muscle-driven motion Nov 30, 2022 - demoa Schmitt, Syn, 2022, "demoa-base: a biophysics simulator for muscle-driven motion", https://doi.org/10.18419/darus-2550, DaRUS, V4 For more information, such as installation, requirements and user guide, please see the demoa-manual.pdf
Replication Data for: Learning Groundwater Contaminant Diffusion-Sorption Processes with a Finite Volume Neural Network 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 t...
gspine: A Human Spine Model Built Using Literature Data Oct 6, 2022 - demoa Hammer, Maria; Riede, Julia Maria; Meszaros-Beller, Laura; Schmitt, Syn, 2022, "gspine: A Human Spine Model Built Using Literature Data", https://doi.org/10.18419/darus-2814, DaRUS, V3 A fully articulating human spine model parametrised using generic literature data for the geometry of the skeleton including attachment points for ligaments and muscles. The model is prepared to run muscle-driven simulation using a simple biological motor control model. The file...

Supporting Information: Chemical Potential Differences in Nanoscopic Teflon/Kapton Capillaries

Jun 6, 2024 - PN 1-8

Stärk, Philipp; Schlaich, Alexander, 2024, "Supporting Information: Chemical Potential Differences in Nanoscopic Teflon/Kapton Capillaries", https://doi.org/10.18419/darus-3149, DaRUS, V1

This is the repository holding the supporting information for atomistic Molecular Dynamics Simulations of Teflon/Kapton capillaries. Here we list the simulation input scripts as well as analysis scripts. See the README file for more information.

Material for the paper "The possible role of lipid bilayer properties in the evolutionary disappearance of betaine lipids in seed plants."

Jul 18, 2023 - BLinK

Schlaich, Alexander, 2023, "Material for the paper "The possible role of lipid bilayer properties in the evolutionary disappearance of betaine lipids in seed plants."", https://doi.org/10.18419/darus-2360, DaRUS, V1

Simulation input scripts to produce the data presented in the manuscript "The possible role of lipid bilayer properties in the evolutionary disappearance of betaine lipids in seed plants." All simulations were carried out using the GROMACS simulation package. The folders contain...

Pre-trained and fine-tuned ANI models for: Transfer learning for chemically accurate interatomic neural network potentials

Feb 20, 2023 - PN 6

Zaverkin, Viktor; Holzmüller, David; Bonfirraro, Luca; Kästner, Johannes, 2023, "Pre-trained and fine-tuned ANI models for: Transfer learning for chemically accurate interatomic neural network potentials", https://doi.org/10.18419/darus-3299, DaRUS, V1

Pre-trained and fine-tuned ANI models using the Gaussian Moments Neural Network (GM-NN) approach. Code for GM-NN implemented within the Tensorflow framework, including the respective documentation and tutorials, can be found on GitLab. The data represents TensorFlow v2 checkpoint...

Gaussian active learning on multi-resolution arbitrary polynomial chaos emulator

Feb 15, 2023 - PN 5

Kohlhaas, Rebecca; Kröker, Ilja; Oladyshkin, Sergey; Nowak, Wolfgang, 2023, "Gaussian active learning on multi-resolution arbitrary polynomial chaos emulator", https://doi.org/10.18419/darus-2829, DaRUS, V1

This folder contains the code for the aMR-PC toolbox by Ilja Kröker in the version used for the code in GALMAP_code. This toolbox was also used for Kröker et al. 2022 Link to current version of the toolbox here Data This folder contains inputs and simulated outputs of the CO_2 be...

Code for: Seasonal Dynamics of Gaseous CO2 Concentrations in a Karst Cave Correspond With Aqueous Concentrations in a Stagnant Water Column

Feb 13, 2023 - C04: Pore-scale and REV-scale approaches to biological and chemical pore-space alteration in porous media

Keim, Leon; Class, Holger; Schirmer, Larissa; Strauch, Bettina; Wendel, Kai; Zimmer, Martin, 2023, "Code for: Seasonal Dynamics of Gaseous CO2 Concentrations in a Karst Cave Correspond With Aqueous Concentrations in a Stagnant Water Column", https://doi.org/10.18419/darus-3276, DaRUS, V1

This dataset contains the DuMux code for the simulations in https://doi.org/10.3390/geosciences13020051 For the detailed list of software used, please have a look at the file install_class2023.sh. To run the simulations at your own computer, please conduct the following steps: In...

Data for: Measurement Campaign of Gaseous CO2 Concentrations in a Karst Cave with Aqueous Concentrations in a Stagnant Water Column 2021-2022.

Feb 13, 2023 - C04: Pore-scale and REV-scale approaches to biological and chemical pore-space alteration in porous media

Keim, Leon; Class, Holger; Schirmer, Larissa; Wendel, Kai; Strauch, Bettina; Zimmer, Martin, 2023, "Data for: Measurement Campaign of Gaseous CO2 Concentrations in a Karst Cave with Aqueous Concentrations in a Stagnant Water Column 2021-2022.", https://doi.org/10.18419/darus-3271, DaRUS, V1

This dataset contains data generated during the measurement campaign inside the karst cave. The CO2 sensors in the cave air will continue to measure (as of Feb. 2023). For details on the site etc. see https://doi.org/10.3390/geosciences13020051 To create the graphs in the Class e...

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 an...

demoa-base: a biophysics simulator for muscle-driven motion

Nov 30, 2022 - demoa

Schmitt, Syn, 2022, "demoa-base: a biophysics simulator for muscle-driven motion", https://doi.org/10.18419/darus-2550, DaRUS, V4

For more information, such as installation, requirements and user guide, please see the demoa-manual.pdf

Replication Data for: Learning Groundwater Contaminant Diffusion-Sorption Processes with a Finite Volume Neural Network

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 t...

gspine: A Human Spine Model Built Using Literature Data

Oct 6, 2022 - demoa

Hammer, Maria; Riede, Julia Maria; Meszaros-Beller, Laura; Schmitt, Syn, 2022, "gspine: A Human Spine Model Built Using Literature Data", https://doi.org/10.18419/darus-2814, DaRUS, V3

A fully articulating human spine model parametrised using generic literature data for the geometry of the skeleton including attachment points for ligaments and muscles. The model is prepared to run muscle-driven simulation using a simple biological motor control model. The file...

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