HAL (France)
open access
Physics-augmented neural networks for predicting nonlinear vibration energy harvesters
This study investigates the use of physics-augmented neural networks to predict the frequency-response behavior of a nonlinear piezoelectric vibration energy harvester (PVEH). An analytical model is first developed under the assumption of geometrical linearity, incorporating a Duffing-type nonlinear
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