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Micro-Electro-Mechanical-Systems are complex structures, often involvingnonlinearites of geometric and multiphysics nature, that are used as sensorsand actuators in countless applications. Starting from full-orderrepresentations, we apply deep learning techniques to generate accurate,efficient and real-time reduced order models to be used as virtual twin for thesimulation and optimization of higher-level complex systems. We extensivelytest the reliability of the proposed procedures on micromirrors, arches andgyroscopes, also displaying intricate dynamical evolutions like internalresonances. In particular, we discuss the accuracy of the deep learningtechnique and its ability to replicate and converge to the invariant manifoldspredicted using the recently developed direct parametrization approach thatallows extracting the nonlinear normal modes of large finite element models.Finally, by addressing an electromechanical gyroscope, we show that thenon-intrusive deep learning approach generalizes easily to complex multiphysicsproblems

Virtual twins of nonlinear vibrating multiphysics microstructures: physics-based versus deep learning-based approaches