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.

21,051 to 21,060 of 21,100 Results

README.md Nov 2, 2020 - Supplemental Material for Comparative Visual Gaze Analysis for Virtual Board Games Markdown Text - 6.3 KB - MD5: d3274de99af618853a65ef6a2704dd45 Information about the analysis system
release.zip Nov 2, 2020 - Supplemental Material for Comparative Visual Gaze Analysis for Virtual Board Games ZIP Archive - 29.5 MB - MD5: 2e358ba20bb0d45552fa4e6066220390 Executable files of the analysis system
sourceCode.zip Nov 2, 2020 - Supplemental Material for Comparative Visual Gaze Analysis for Virtual Board Games ZIP Archive - 29.5 MB - MD5: cf60c265a2cb8e048f2ca44d571ad19a Java source code of the analysis system
PN 6-4(Universität Stuttgart) Oct 27, 2020PN 6 SimTech Project PN 6-4 "Visual Analytics for Deep Learning"
Trained ANN Parameters for Physics-inspired Artificial Neural Network for Dynamic System Jul 21, 2020 - PINN Dynamic System Praditia, Timothy, 2020, "Trained ANN Parameters for Physics-inspired Artificial Neural Network for Dynamic System", https://doi.org/10.18419/DARUS-634, DaRUS, V1 This dataset contains four .xlsx files containing trained values of the ANN weights and biases, along with the hyperparameter values at the end of the training (with noisy dataset). These four files correspond to four different regularization methods.
ANN_params_MSE_L2_PHY.xlsx Jul 21, 2020 - Trained ANN Parameters for Physics-inspired Artificial Neural Network for Dynamic System MS Excel Spreadsheet - 19.1 KB - MD5: 9b88868eeef277937d0c729615e217de ANN parameters for the regularization method "MSE+L2+PHY"
ANN_params_MSE_L2.xlsx Jul 21, 2020 - Trained ANN Parameters for Physics-inspired Artificial Neural Network for Dynamic System MS Excel Spreadsheet - 18.7 KB - MD5: 9a85fe1d359cb162a8c4bc183545ce46 ANN parameters for the regularization method "MSE+L2"
ANN_params_MSE_PHY.xlsx Jul 21, 2020 - Trained ANN Parameters for Physics-inspired Artificial Neural Network for Dynamic System MS Excel Spreadsheet - 19.0 KB - MD5: 1b13a37e8fb1bf8ee2630519982ae0e4 ANN parameters for the regularization method "MSE+PHY"
ANN_params_MSE.xlsx Jul 21, 2020 - Trained ANN Parameters for Physics-inspired Artificial Neural Network for Dynamic System MS Excel Spreadsheet - 18.5 KB - MD5: ed241c309250b1d17eb682b750fa1244 ANN parameters for the regularization method "MSE"
README.md Jul 21, 2020 - Trained ANN Parameters for Physics-inspired Artificial Neural Network for Dynamic System Markdown Text - 3.0 KB - MD5: 3aa442599a974dcf6b70ad2c45da506c README file explaining how the data is formatted

README.md

Nov 2, 2020 - Supplemental Material for Comparative Visual Gaze Analysis for Virtual Board Games

Markdown Text - 6.3 KB -

Information about the analysis system

release.zip

Nov 2, 2020 - Supplemental Material for Comparative Visual Gaze Analysis for Virtual Board Games

ZIP Archive - 29.5 MB -

Executable files of the analysis system

sourceCode.zip

Nov 2, 2020 - Supplemental Material for Comparative Visual Gaze Analysis for Virtual Board Games

ZIP Archive - 29.5 MB -

Java source code of the analysis system

PN 6-4(Universität Stuttgart)

Oct 27, 2020PN 6

SimTech Project PN 6-4 "Visual Analytics for Deep Learning"

Trained ANN Parameters for Physics-inspired Artificial Neural Network for Dynamic System

Jul 21, 2020 - PINN Dynamic System

Praditia, Timothy, 2020, "Trained ANN Parameters for Physics-inspired Artificial Neural Network for Dynamic System", https://doi.org/10.18419/DARUS-634, DaRUS, V1

This dataset contains four .xlsx files containing trained values of the ANN weights and biases, along with the hyperparameter values at the end of the training (with noisy dataset). These four files correspond to four different regularization methods.

ANN_params_MSE_L2_PHY.xlsx

Jul 21, 2020 - Trained ANN Parameters for Physics-inspired Artificial Neural Network for Dynamic System

MS Excel Spreadsheet - 19.1 KB -

ANN parameters for the regularization method "MSE+L2+PHY"

ANN_params_MSE_L2.xlsx

Jul 21, 2020 - Trained ANN Parameters for Physics-inspired Artificial Neural Network for Dynamic System

MS Excel Spreadsheet - 18.7 KB -

ANN parameters for the regularization method "MSE+L2"

ANN_params_MSE_PHY.xlsx

Jul 21, 2020 - Trained ANN Parameters for Physics-inspired Artificial Neural Network for Dynamic System

MS Excel Spreadsheet - 19.0 KB -

ANN parameters for the regularization method "MSE+PHY"

ANN_params_MSE.xlsx

Jul 21, 2020 - Trained ANN Parameters for Physics-inspired Artificial Neural Network for Dynamic System

MS Excel Spreadsheet - 18.5 KB -

ANN parameters for the regularization method "MSE"

README.md

Jul 21, 2020 - Trained ANN Parameters for Physics-inspired Artificial Neural Network for Dynamic System

Markdown Text - 3.0 KB -

README file explaining how the data is formatted

Add Data

Share Dataverse

Link Dataverse

Reset Modifications