Stuttgart Center for Simulation Science (SC SimTech)

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221 to 230 of 293 Results

PyPlant: A Python Framework for Cached Function Pipelines Jan 24, 2022 - PN 6-6 Tkachev, Gleb, 2022, "PyPlant: A Python Framework for Cached Function Pipelines", https://doi.org/10.18419/DARUS-2249, DaRUS, V1 PyPlant is a simple coroutine-based framework for writing data processing pipelines. PyPlant's goal is to simplify caching of intermediate results in the pipeline and avoid re-running expensive early stages of the pipeline, when only the later stages have changed.
Supplementary material for 'Binding free energies for the SAMPL8 CB8 "Drugs of Abuse" challenge from umbrella sampling combined with Hamiltonian replica exchange' Jan 16, 2022 - Institute of Thermodynamics and Thermal Process Engineering Markthaler, Daniel; Kraus, Hamzeh; Hansen, Niels, 2022, "Supplementary material for 'Binding free energies for the SAMPL8 CB8 "Drugs of Abuse" challenge from umbrella sampling combined with Hamiltonian replica exchange'", https://doi.org/10.18419/DARUS-2109, DaRUS, V1 Binding affinities of seven drug molecules (G1-G7) towards a common receptor (cucurbit[8]uril, CB8) were estimated from molecular dynamics (MD) simulations in the scope of the recent SAMPL8 CB8 "Drugs of Abuse" challenge using the GROMACS MD package. To compare with experimental data, a scheme for correcting the raw simulation estimates was propose...
Supplementary material for 'Umbrella sampling and double decoupling data for methanol binding to Candida antarctica lipase B' Nov 25, 2021 - Molecular Simulation Markthaler, Daniel; Hansen, Niels, 2021, "Supplementary material for 'Umbrella sampling and double decoupling data for methanol binding to Candida antarctica lipase B'", https://doi.org/10.18419/DARUS-2104, DaRUS, V1 This dataset contains all relevant simulation input files (topologies, coordinates, simulation parameters), generated simulation output (final configurations, time series of collective variables) together with scripts used for set-up and analysis of the umbrella sampling and double decoupling simulations.
Code of the tBME method Nov 24, 2021 - tBME project Hsueh, Han-Fang, 2021, "Code of the tBME method", https://doi.org/10.18419/DARUS-1836, DaRUS, V1 Code and data for the publication "Diagnosis of model errors with a sliding time-window Bayesian analysis" in Journal Water Resource Research (preprint https://arxiv.org/abs/2107.09399) . The folder "tau_plot" includes the files and data to generate the tBME analysis plots for Case 1, Case 2, Case 3, and real data Case as shown in the publication....
Code for relative permeabilities for two-phase flow between parallel plates with slip conditions Nov 24, 2021 - PN 1-6 Schulz, Sebastian; Bringedal, Carina; Ackermann, Sina, 2021, "Code for relative permeabilities for two-phase flow between parallel plates with slip conditions", https://doi.org/10.18419/DARUS-2241, DaRUS, V1 This MATLAB code calculates relative permeabilities of two fluids flowing between two parallel plates, depending on their viscosities, saturation and boundary conditions at the top and bottom plates. The underlying assumptions behind the derivation are shown in the pdf "overview_relativepermeabilities.pdf", where also the resulting equations are li...
tBME project(Universität Stuttgart) Nov 23, 2021Stochastic Simulation and Safety Research for Hydrosystems (LS3) tBME project
PN 1-6(Universität Stuttgart) Nov 22, 2021PN 1 SimTech Project PN 1-6 "Upscaling of two-phase porous media flow based on fluid morphology."
Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments Oct 15, 2021 - PN 6 Zaverkin, Viktor; Holzmüller, David; Steinwart, Ingo; Kästner, Johannes, 2021, "Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments", https://doi.org/10.18419/DARUS-2136, DaRUS, V1 Code and documentation for the improved Gaussian Moments Neural Network (GM-NN). An updated version can be found on GitLab
Replication Data for: "S4: Self-Supervised learning of Spatiotemporal Similarity" Oct 5, 2021 - PN 6-6 Tkachev, Gleb, 2021, "Replication Data for: "S4: Self-Supervised learning of Spatiotemporal Similarity"", https://doi.org/10.18419/DARUS-2174, DaRUS, V1 We train a self-supervised siamese model that enables querying for similar behavior on spatiotemporal volumes. Here we provide the code and data needed to reproduce the representative figures of the paper. See the notes and the included readme file for details.
PN 6-6(Universität Stuttgart) Sep 29, 2021PN 6 SimTech Project PN 6-6 "Machine Learning for Data-driven Visualization"

PyPlant: A Python Framework for Cached Function Pipelines

Jan 24, 2022 - PN 6-6

Tkachev, Gleb, 2022, "PyPlant: A Python Framework for Cached Function Pipelines", https://doi.org/10.18419/DARUS-2249, DaRUS, V1

PyPlant is a simple coroutine-based framework for writing data processing pipelines. PyPlant's goal is to simplify caching of intermediate results in the pipeline and avoid re-running expensive early stages of the pipeline, when only the later stages have changed.

Supplementary material for 'Binding free energies for the SAMPL8 CB8 "Drugs of Abuse" challenge from umbrella sampling combined with Hamiltonian replica exchange'

Jan 16, 2022 - Institute of Thermodynamics and Thermal Process Engineering

Markthaler, Daniel; Kraus, Hamzeh; Hansen, Niels, 2022, "Supplementary material for 'Binding free energies for the SAMPL8 CB8 "Drugs of Abuse" challenge from umbrella sampling combined with Hamiltonian replica exchange'", https://doi.org/10.18419/DARUS-2109, DaRUS, V1

Binding affinities of seven drug molecules (G1-G7) towards a common receptor (cucurbit[8]uril, CB8) were estimated from molecular dynamics (MD) simulations in the scope of the recent SAMPL8 CB8 "Drugs of Abuse" challenge using the GROMACS MD package. To compare with experimental data, a scheme for correcting the raw simulation estimates was propose...

Supplementary material for 'Umbrella sampling and double decoupling data for methanol binding to Candida antarctica lipase B'

Nov 25, 2021 - Molecular Simulation

Markthaler, Daniel; Hansen, Niels, 2021, "Supplementary material for 'Umbrella sampling and double decoupling data for methanol binding to Candida antarctica lipase B'", https://doi.org/10.18419/DARUS-2104, DaRUS, V1

This dataset contains all relevant simulation input files (topologies, coordinates, simulation parameters), generated simulation output (final configurations, time series of collective variables) together with scripts used for set-up and analysis of the umbrella sampling and double decoupling simulations.

Code of the tBME method

Nov 24, 2021 - tBME project

Hsueh, Han-Fang, 2021, "Code of the tBME method", https://doi.org/10.18419/DARUS-1836, DaRUS, V1

Code and data for the publication "Diagnosis of model errors with a sliding time-window Bayesian analysis" in Journal Water Resource Research (preprint https://arxiv.org/abs/2107.09399) . The folder "tau_plot" includes the files and data to generate the tBME analysis plots for Case 1, Case 2, Case 3, and real data Case as shown in the publication....

Code for relative permeabilities for two-phase flow between parallel plates with slip conditions

Nov 24, 2021 - PN 1-6

Schulz, Sebastian; Bringedal, Carina; Ackermann, Sina, 2021, "Code for relative permeabilities for two-phase flow between parallel plates with slip conditions", https://doi.org/10.18419/DARUS-2241, DaRUS, V1

This MATLAB code calculates relative permeabilities of two fluids flowing between two parallel plates, depending on their viscosities, saturation and boundary conditions at the top and bottom plates. The underlying assumptions behind the derivation are shown in the pdf "overview_relativepermeabilities.pdf", where also the resulting equations are li...

tBME project(Universität Stuttgart)

Nov 23, 2021Stochastic Simulation and Safety Research for Hydrosystems (LS3)

tBME project

PN 1-6(Universität Stuttgart)

Nov 22, 2021PN 1

SimTech Project PN 1-6 "Upscaling of two-phase porous media flow based on fluid morphology."

Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments

Oct 15, 2021 - PN 6

Zaverkin, Viktor; Holzmüller, David; Steinwart, Ingo; Kästner, Johannes, 2021, "Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments", https://doi.org/10.18419/DARUS-2136, DaRUS, V1

Code and documentation for the improved Gaussian Moments Neural Network (GM-NN). An updated version can be found on GitLab

Replication Data for: "S4: Self-Supervised learning of Spatiotemporal Similarity"

Oct 5, 2021 - PN 6-6

Tkachev, Gleb, 2021, "Replication Data for: "S4: Self-Supervised learning of Spatiotemporal Similarity"", https://doi.org/10.18419/DARUS-2174, DaRUS, V1

We train a self-supervised siamese model that enables querying for similar behavior on spatiotemporal volumes. Here we provide the code and data needed to reproduce the representative figures of the paper. See the notes and the included readme file for details.

PN 6-6(Universität Stuttgart)

Sep 29, 2021PN 6

SimTech Project PN 6-6 "Machine Learning for Data-driven Visualization"

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