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PINN Dynamic System (Universität Stuttgart)
This dataverse contains dataset and codes for the submitted publication: Praditia, T., Walser, T., Oladyshkin, S. and Nowak, W. (2020): Physics-inspired Artificial Neural Network structure improves prediction: Application to a Thermochemical Energy Storage System
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Jul 21, 2020
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
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 ne...
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