EXC 2075 Project Networks

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Publication Year: 2020 License: CC BY 4.0

1 to 10 of 16 Results

2d microstructure data Feb 15, 2021 - Data Analytics in Engineering Lißner, Julian, 2020, "2d microstructure data", https://doi.org/10.18419/darus-1151, DaRUS, V2 The hdf5 file contains image data of inclusion based microstructured material and the homogenized effective heat conductivity thereof. The microstructure is defined with a representative volume element with periodic boundary conditions. 30.000 images are contained and split into...
2d_microstructures.h5 Feb 15, 2021 - 2d microstructure data Hierarchical Data Format - 967.9 MB - MD5: 8d93bd9af6585b0f63257c571920752c hdf5 file containing all the data
compute_regression_data.py Feb 15, 2021 - 2d microstructure data Python Source Code - 2.3 KB - MD5: 8088ddd98656e04611bdf589220baf99 code to recompute/reproduce the data used for the regression problem
example_data_access.py Feb 15, 2021 - 2d microstructure data Python Source Code - 1.9 KB - MD5: be74203ddeb47af439f3cbc33594ac92 an example on how to access data stored in the hdf5 file
processing_functions.py Feb 15, 2021 - 2d microstructure data Python Source Code - 3.8 KB - MD5: 24355f5d0f71131cf836137a375fce75 dependency file for 'compute_regression_data.py'
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"

2d microstructure data

Feb 15, 2021 - Data Analytics in Engineering

Lißner, Julian, 2020, "2d microstructure data", https://doi.org/10.18419/darus-1151, DaRUS, V2

The hdf5 file contains image data of inclusion based microstructured material and the homogenized effective heat conductivity thereof. The microstructure is defined with a representative volume element with periodic boundary conditions. 30.000 images are contained and split into...

2d_microstructures.h5

Feb 15, 2021 - 2d microstructure data

Hierarchical Data Format - 967.9 MB -

hdf5 file containing all the data

compute_regression_data.py

Feb 15, 2021 - 2d microstructure data

Python Source Code - 2.3 KB -

code to recompute/reproduce the data used for the regression problem

example_data_access.py

Feb 15, 2021 - 2d microstructure data

Python Source Code - 1.9 KB -

an example on how to access data stored in the hdf5 file

processing_functions.py

Feb 15, 2021 - 2d microstructure data

Python Source Code - 3.8 KB -

dependency file for 'compute_regression_data.py'

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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