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Giorgio Gobat¹, Stefania Fresca², Andrea Manzoni²

¹Department of Civil and Environmental Engineering, Politecnico di Milano, Piazza Leonardo da Vinci, 32, 20133 Milano, Italy.

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|March 30, 2023

Summary

This summary is machine-generated.

Deep learning creates accurate reduced-order models for micro-electro-mechanical-systems (MEMS). This enables efficient real-time simulation and optimization of complex MEMS devices like gyroscopes.

Keywords:

data-driven model deep learning invariant manifolds nonlinear dynamics reduced order modeling

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Area of Science:

Engineering
Computational Science
Physics

Background:

Micro-electro-mechanical-systems (MEMS) are vital sensors and actuators with complex nonlinear behaviors.
Full-order models of MEMS are computationally intensive, limiting real-time simulation and optimization.

Purpose of the Study:

To develop accurate and efficient reduced-order models (ROMs) for MEMS using deep learning.
To enable real-time simulation and optimization of complex systems incorporating MEMS.

Main Methods:

Application of deep learning techniques to full-order MEMS representations.
Testing and validation on diverse MEMS structures like micromirrors, arches, and gyroscopes.
Comparison with direct parametrization for nonlinear normal mode extraction.

Main Results:

Deep learning successfully generated accurate, real-time ROMs for MEMS.
The models replicated invariant manifolds and converged to nonlinear normal modes.
The approach demonstrated generalization to complex electromechanical multiphysics problems.

Conclusions:

Deep learning offers a powerful, non-intrusive method for creating efficient MEMS simulations.
This technique facilitates the optimization of complex systems involving MEMS devices.
The approach is versatile and applicable to various MEMS designs and multiphysics scenarios.