Virtual twins of nonlinear vibrating multiphysics microstructures: physics-based versus deep learning-based approaches. (arXiv:2205.05928v1 [math.DS])

Web: http://arxiv.org/abs/2205.05928

Micro-Electro-Mechanical-Systems are complex structures, often involving
nonlinearites of geometric and multiphysics nature, that are used as sensors
and actuators in countless applications. Starting from full-order
representations, we apply deep learning techniques to generate accurate,
efficient and real-time reduced order models to be used as virtual twin for the
simulation and optimization of higher-level complex systems. We extensively
test the reliability of the proposed procedures on micromirrors, arches and
gyroscopes, also displaying intricate dynamical evolutions like internal
resonances. In particular, we …

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