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Part 1: Document Description
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Citation |
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Title: |
Trained ANN Parameters for Physics-inspired Artificial Neural Network for Dynamic System |
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Identification Number: |
doi:10.18419/darus-634 |
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Distributor: |
DaRUS |
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Date of Distribution: |
2020-07-21 |
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Version: |
1 |
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Bibliographic Citation: |
Praditia, Timothy, 2020, "Trained ANN Parameters for Physics-inspired Artificial Neural Network for Dynamic System", https://doi.org/10.18419/darus-634, DaRUS, V1 |
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Citation |
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Title: |
Trained ANN Parameters for Physics-inspired Artificial Neural Network for Dynamic System |
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Identification Number: |
doi:10.18419/darus-634 |
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Authoring Entity: |
Praditia, Timothy (Universität Stuttgart) |
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Other identifications and acknowledgements: |
Walser, Thilo |
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Other identifications and acknowledgements: |
Oladyshkin, Sergey |
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Other identifications and acknowledgements: |
Nowak, Wolfgang |
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Grant Number: |
EXC-2075 – 390740016 |
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Distributor: |
DaRUS |
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Access Authority: |
Nowak, Wolfgang |
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Depositor: |
Praditia, Timothy |
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Date of Deposit: |
2020-02-07 |
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Holdings Information: |
https://doi.org/10.18419/darus-634 |
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Study Scope |
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Keywords: |
Computer and Information Science, Earth and Environmental Sciences, Artificial Neural Network, Physics-based Regularization, Physics Inspired Neural Network, Thermochemical Energy Storage |
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Abstract: |
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. |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Other Study Description Materials |
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Related Studies |
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Praditia, T. (2020): Input-Output Dataset for Physics-inspired Artificial Neural Network for Dynamic System, <a href="https://doi.org/10.18419/darus-633">doi: 10.18419/darus-633</a>, DaRUS. |
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Related Publications |
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Citation |
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Title: |
Praditia, T., Walser, T., Oladyshkin, S. and Nowak, W.: Improving Thermochemical Energy Storage dynamics forecast with Physics-Inspired Neural Network architecture. Energies 2020 |
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Bibliographic Citation: |
Praditia, T., Walser, T., Oladyshkin, S. and Nowak, W.: Improving Thermochemical Energy Storage dynamics forecast with Physics-Inspired Neural Network architecture. Energies 2020 |
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Label: |
ANN_params_MSE.xlsx |
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Text: |
ANN parameters for the regularization method "MSE" |
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Notes: |
application/vnd.openxmlformats-officedocument.spreadsheetml.sheet |
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Label: |
ANN_params_MSE_L2.xlsx |
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Text: |
ANN parameters for the regularization method "MSE+L2" |
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Notes: |
application/vnd.openxmlformats-officedocument.spreadsheetml.sheet |
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Label: |
ANN_params_MSE_L2_PHY.xlsx |
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Text: |
ANN parameters for the regularization method "MSE+L2+PHY" |
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Notes: |
application/vnd.openxmlformats-officedocument.spreadsheetml.sheet |
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Label: |
ANN_params_MSE_PHY.xlsx |
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Text: |
ANN parameters for the regularization method "MSE+PHY" |
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Notes: |
application/vnd.openxmlformats-officedocument.spreadsheetml.sheet |
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Label: |
README.md |
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Text: |
README file explaining how the data is formatted |
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Notes: |
text/markdown |