Stuttgart Center for Simulation Science (SC SimTech)

EXC 2075 Project Networks

SIGDIUS

Projects without PN Affiliation

Featured Dataverses

In order to use this feature you must have at least one published or linked dataverse.

Publish Dataverse

Are you sure you want to publish your dataverse? Once you do so it must remain published.

Publish Dataverse

This dataverse cannot be published because the dataverse it is in has not been published.

Delete Dataverse

Are you sure you want to delete your dataverse? You cannot undelete this dataverse.

681 to 690 of 25,631 Results

Replication Data for: Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model Jul 22, 2026 - Studying Dynamical Systems for Intelligible Modeling and Unconventional Computing Weinmann, Max; Klopotek, Miriam, 2026, "Replication Data for: Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model", https://doi.org/10.18419/DARUS-6128, DaRUS, V1 This dataset contains all relevant data to reproduce the results of the paper titled "Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model" and supplementary figures. "code.tar.xz" contains the code to generate the datasets and model checkpoints used in the analysis. Information on the code incl...
checkpoints.tar.xz Jul 22, 2026 - Replication Data for: Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model XZ Archive - 515.9 GB - MD5: d24de0e5275b5f794548bab513cff69f CheckpointsDataModelParameters Contains the resulting model checkpoints, including the used training configuration parameters and some metrics computed based on the datasets. The model checkpoints where saved during training with different hyper-parameters.
code.tar.xz Jul 22, 2026 - Replication Data for: Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model XZ Archive - 133.4 KB - MD5: f8710213b5347894e074dcfd86085fe7 Code Contains the code to generate the datasets and model checkpoints used in the analysis. Information on the code including instructions on how to reproduce the data can be found in the file README.md.
figures.tar.xz Jul 22, 2026 - Replication Data for: Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model XZ Archive - 276.8 MB - MD5: c304a7b7f425cea51012c45b2294e2fe DataFigures Contains supplementary figures for the paper. These contain figures for all the different hyperparameters and datasets which are not shown in the paper.
ising_data.tar.xz Jul 22, 2026 - Replication Data for: Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model XZ Archive - 21.1 MB - MD5: 846676e1c3d289bc9db1542c22a54b76 Data Contains the resulting datasets. The datasets where generated with Markov-Chain-Monte-Carlo based on Glauber Dynamics of the Ising model and used for training and validation.
Replication Data for: Symplecticity-Preserving Prediction of Hamiltonian Dynamics by Generalized Kernel Interpolation Jul 17, 2026 - Projects without PN Affiliation Herkert, Robin, 2026, "Replication Data for: Symplecticity-Preserving Prediction of Hamiltonian Dynamics by Generalized Kernel Interpolation", https://doi.org/10.18419/DARUS-5644, DaRUS, V1 This dataset includes the code and numerical data to reproduce the results from the paper titled "Symplecticity-Preserving Prediction of Hamiltonian Dynamics by Generalized Kernel Interpolation" (2026). It contains the nonparametric variant, in which the surrogate model is trained for one fixed Hamiltonian system and one prescribed macro time step...
auxFunctions.py Jul 17, 2026 - Replication Data for: Symplecticity-Preserving Prediction of Hamiltonian Dynamics by Generalized Kernel Interpolation Python Source Code - 1.2 KB - MD5: 90c626c3ace81090ebde9564fc097c8c
data.py Jul 17, 2026 - Replication Data for: Symplecticity-Preserving Prediction of Hamiltonian Dynamics by Generalized Kernel Interpolation Python Source Code - 2.5 KB - MD5: 4d3aeea936d046ca90dd6cadc2d0491f
implicitSurrogate_NonlinearSpring_dT=0.025 Jul 17, 2026 - Replication Data for: Symplecticity-Preserving Prediction of Hamiltonian Dynamics by Generalized Kernel Interpolation Unknown - 32.2 MB - MD5: 8a44375c8988a73ee220c61d255b06fa
implicitSurrogate_NonlinearSpring_dT=0.05 Jul 17, 2026 - Replication Data for: Symplecticity-Preserving Prediction of Hamiltonian Dynamics by Generalized Kernel Interpolation Unknown - 32.2 MB - MD5: f66180702db329e8f68c73575ac26357

Replication Data for: Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model

Jul 22, 2026 - Studying Dynamical Systems for Intelligible Modeling and Unconventional Computing

Weinmann, Max; Klopotek, Miriam, 2026, "Replication Data for: Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model", https://doi.org/10.18419/DARUS-6128, DaRUS, V1

This dataset contains all relevant data to reproduce the results of the paper titled "Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model" and supplementary figures. "code.tar.xz" contains the code to generate the datasets and model checkpoints used in the analysis. Information on the code incl...

checkpoints.tar.xz

Jul 22, 2026 - Replication Data for: Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model

XZ Archive - 515.9 GB -

CheckpointsDataModelParameters

Contains the resulting model checkpoints, including the used training configuration parameters and some metrics computed based on the datasets. The model checkpoints where saved during training with different hyper-parameters.

code.tar.xz

Jul 22, 2026 - Replication Data for: Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model

XZ Archive - 133.4 KB -

Code

Contains the code to generate the datasets and model checkpoints used in the analysis. Information on the code including instructions on how to reproduce the data can be found in the file README.md.

figures.tar.xz

Jul 22, 2026 - Replication Data for: Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model

XZ Archive - 276.8 MB -

DataFigures

Contains supplementary figures for the paper. These contain figures for all the different hyperparameters and datasets which are not shown in the paper.

ising_data.tar.xz

Jul 22, 2026 - Replication Data for: Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model

XZ Archive - 21.1 MB -

Data

Contains the resulting datasets. The datasets where generated with Markov-Chain-Monte-Carlo based on Glauber Dynamics of the Ising model and used for training and validation.

Replication Data for: Symplecticity-Preserving Prediction of Hamiltonian Dynamics by Generalized Kernel Interpolation

Jul 17, 2026 - Projects without PN Affiliation

Herkert, Robin, 2026, "Replication Data for: Symplecticity-Preserving Prediction of Hamiltonian Dynamics by Generalized Kernel Interpolation", https://doi.org/10.18419/DARUS-5644, DaRUS, V1

This dataset includes the code and numerical data to reproduce the results from the paper titled "Symplecticity-Preserving Prediction of Hamiltonian Dynamics by Generalized Kernel Interpolation" (2026). It contains the nonparametric variant, in which the surrogate model is trained for one fixed Hamiltonian system and one prescribed macro time step...

auxFunctions.py

Jul 17, 2026 - Replication Data for: Symplecticity-Preserving Prediction of Hamiltonian Dynamics by Generalized Kernel Interpolation

Python Source Code - 1.2 KB -

data.py

Jul 17, 2026 - Replication Data for: Symplecticity-Preserving Prediction of Hamiltonian Dynamics by Generalized Kernel Interpolation

Python Source Code - 2.5 KB -

implicitSurrogate_NonlinearSpring_dT=0.025

Jul 17, 2026 - Replication Data for: Symplecticity-Preserving Prediction of Hamiltonian Dynamics by Generalized Kernel Interpolation

Unknown - 32.2 MB -

implicitSurrogate_NonlinearSpring_dT=0.05

Jul 17, 2026 - Replication Data for: Symplecticity-Preserving Prediction of Hamiltonian Dynamics by Generalized Kernel Interpolation

Unknown - 32.2 MB -

Add Data

Share Dataverse

Link Dataverse

Reset Modifications