127,521 to 127,530 of 128,245 Results
Python Source Code - 12.7 KB -
MD5: ed44dd3e200cd8019166835d3b25ca23
This produces the plot for a coal system with up to two amendment additions starting on day 76. |
Python Source Code - 12.1 KB -
MD5: 826e48d7bd300b86fcfb22b368ac3a3b
This produces the plot for a coal system with up to three amendment additions (from day 0 on) |
Python Source Code - 12.0 KB -
MD5: 24096d9a676972bf0b29826aa104259f
This produces the plot for a glass beads system with up to three amendment additions. |
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. |
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" |
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" |
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" |
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" |
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 |
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... |