PN 5

SimTech EXC 2075 Project Network 5 "On-the-fly model modification, error control, and simulation adaptivity"

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.

1,501 to 1,510 of 1,516 Results

Timematch_all_IP.mat Jun 18, 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 MATLAB Data - 5.5 MB - MD5: 548da79dda1450bd18c85d10276c1510 Data cell array consisting the entire time-match sequence for each realization.
Timematch_final_IP.mat Jun 18, 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 MATLAB Data - 2.3 KB - MD5: af688e30918cac40a80dcdf935027986 Data each row contains the loop counter for the final image time-match of that realization
Time_match.m Jun 18, 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 Plain Text - 2.1 KB - MD5: 13577a254906907dc9aed2f6c6fb498b Code volume based time-matching code
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
Input-Output Dataset for Physics-inspired Artificial Neural Network for Dynamic System Jul 21, 2020 - PINN Dynamic System Praditia, Timothy, 2020, "Input-Output Dataset for Physics-inspired Artificial Neural Network for Dynamic System", https://doi.org/10.18419/DARUS-633, DaRUS, V1 This dataset contains two .mat files, one pre-processed (direct simulation results) and the other one is with added noise. The simulated problem is a thermochemical energy storage problem using CaO/Ca(OH)2 as the material choice. This dataset is used as input-output data pairs necessary for training, validating, and testing the ANN. The input data...

Timematch_all_IP.mat

Jun 18, 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

MATLAB Data - 5.5 MB -

Data

cell array consisting the entire time-match sequence for each realization.

Timematch_final_IP.mat

Jun 18, 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

MATLAB Data - 2.3 KB -

Data

each row contains the loop counter for the final image time-match of that realization

Time_match.m

Jun 18, 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

Plain Text - 2.1 KB -

Code

volume based time-matching code

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

Input-Output Dataset for Physics-inspired Artificial Neural Network for Dynamic System

Jul 21, 2020 - PINN Dynamic System

Praditia, Timothy, 2020, "Input-Output Dataset for Physics-inspired Artificial Neural Network for Dynamic System", https://doi.org/10.18419/DARUS-633, DaRUS, V1

This dataset contains two .mat files, one pre-processed (direct simulation results) and the other one is with added noise. The simulated problem is a thermochemical energy storage problem using CaO/Ca(OH)2 as the material choice. This dataset is used as input-output data pairs necessary for training, validating, and testing the ANN. The input data...

Add Data

Share Dataverse

Link Dataverse

Reset Modifications