10.18419/darus-634Praditia, TimothyTimothyPraditia0000-0003-3619-9122Universität StuttgartTrained ANN Parameters for Physics-inspired Artificial Neural Network for Dynamic SystemDaRUS2020Computer and Information ScienceEarth and Environmental SciencesArtificial Neural NetworkPhysics-based RegularizationPhysics Inspired Neural NetworkThermochemical Energy StorageNowak, WolfgangWolfgangNowakUniversität StuttgartWalser, ThiloThiloWalserOladyshkin, SergeySergeyOladyshkinNowak, WolfgangWolfgangNowak2020-02-072020-07-21195211916119475189953036application/vnd.openxmlformats-officedocument.spreadsheetml.sheetapplication/vnd.openxmlformats-officedocument.spreadsheetml.sheetapplication/vnd.openxmlformats-officedocument.spreadsheetml.sheetapplication/vnd.openxmlformats-officedocument.spreadsheetml.sheettext/markdown1.0CC BY 4.0This 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.Deutsche ForschungsgemeinschaftEXC-2075 – 390740016