PN 5

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

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1,561 to 1,570 of 1,576 Results

mygaussfilt.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 - 497 B - MD5: cf38db012533faddf3c33e38a13426e6 Code code: Gaussian blurring kernel
MyPostProcessing.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 - 8.1 KB - MD5: ca63840a125c066e0803c3d5b0524c0a Code code: main post-processing code (methods of comparison); calls "mygaussfilt.m" and "Moments.m"
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"

mygaussfilt.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 - 497 B -

Code

code: Gaussian blurring kernel

MyPostProcessing.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 - 8.1 KB -

Code

code: main post-processing code (methods of comparison); calls "mygaussfilt.m" and "Moments.m"

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"

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