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1 to 10 of 30 Results
Visual Analytics System for Hidden States in Recurrent Neural Networks
Sep 10, 2021 - PN 6-4
Munz, Tanja; Garcia, Rafael; Weiskopf, Daniel, 2021, "Visual Analytics System for Hidden States in Recurrent Neural Networks", https://doi.org/10.18419/darus-2052, DaRUS, V1
Source code of our visual analytics system for the interpretation of hidden states in recurrent neural networks. This project contains source code for preprocessing data and the visual analytics system. Additionally, we added precomputed data for immediate use in the visual analy...
Usability and Sustainability of Simulation Software(Universität Stuttgart)
Sep 21, 2021Institute for Parallel and Distributed Systems
tBME project(Universität Stuttgart)
Nov 23, 2021Stochastic Simulation and Safety Research for Hydrosystems (LS3)
tBME project
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 doub...
Stochastic Simulation and Safety Research for Hydrosystems (LS3)(Institute for Modelling Hydraulic and Environmental Systems, University of Stuttgart)
Mar 12, 2021Projects without PN Affiliation
This dataverse contains data associated with the projects (as sub-dataverses) of LS3.
Simulation input scripts for "Electronic screening using a virtual Thomas-Fermi fluid for predicting wetting and phase transitions of ionic liquids at metal surfaces"
Aug 20, 2021 - Paper Nature Materials 2021
Schlaich, Alexander, 2021, "Simulation input scripts for "Electronic screening using a virtual Thomas-Fermi fluid for predicting wetting and phase transitions of ionic liquids at metal surfaces"", https://doi.org/10.18419/darus-2115, DaRUS, V1
This dataset includes the basic simulation scripts needed in order to reproduce the data shown in "Electronic screening using a virtual Thomas-Fermi fluid for predicting wetting and phase transitions of ionic liquids at metal surfaces". The folder structure corresponds to the ind...
Sampling Strategies of the Regime-and-memory model (RMM)
Jun 24, 2021 - CAMPOS Project P8: Conceptual Model Uncertainty
Gonzalez-Nicolas Alvarez, Ana, 2021, "Sampling Strategies of the Regime-and-memory model (RMM)", https://doi.org/10.18419/darus-2035, DaRUS, V1, UNF:6:JeAvfovoq369qtbASSmQjg== [fileUNF]
This excel file includes the observation time, Q, concentration, and lag-time used by the sampling strategies. Types of sampling strategies: Time frequency sampling strategies. River discharge frequency sampling strategies. Low Q sampling strategies. High Q sampling strategies. L...
Replication Data for: Overcoming the model-data-fit problem in porous media: A quantitative method to compare invasion-percolation models to high-resolution data
Jun 18, 2021 - Modeling Strategies for Gas migration in Subsurface
Banerjee, Ishani, 2021, "Replication Data for: Overcoming the model-data-fit problem in porous media: A quantitative method to compare invasion-percolation models to high-resolution data", https://doi.org/10.18419/darus-1776, DaRUS, V1
This dataset contains modeling data used to obtain the results and figures in the manuscript: "Overcoming the model-data-fit problem in porous media: A quantitative method to compare invasion-percolation models to high-resolution data." In particular, the model realization data a...
Replication Data for: On the Universality of the Double Descent Peak in Ridgeless Regression
Mar 23, 2021 - PN 6-3
Holzmüller, David, 2021, "Replication Data for: On the Universality of the Double Descent Peak in Ridgeless Regression", https://doi.org/10.18419/darus-1771, DaRUS, V1
This dataset contains code used to generate the figures in the paper On the Universality of the Double Descent Peak in Ridgeless Regression, David Holzmüller, International Conference on Learning Representations 2021. The code is also provided on GitHub. Here, we additionally pro...
Replication Data for: "S4: Self-Supervised learning of Spatiotemporal Similarity"
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.
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