  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 discuss the accuracy of the deep learning
technique and its ability to replicate and converge to the invariant manifolds
predicted using the recently developed direct parametrization approach that
allows extracting the nonlinear normal modes of large finite element models.
Finally, by addressing an electromechanical gyroscope, we show that the
non-intrusive deep learning approach generalizes easily to complex multiphysics
problems
