Modelling the Periodic Response of Micro-Electromechanical Systems through Deep Learning-Based Approaches

We propose a deep learning-based reduced order modelling approach for micro- electromechanical systems. The method allows treating parametrised, fully coupled electromechanical problems in a non-intrusive way and provides solutions across the whole device domain almost in real time, making it suitable for design optimisation and control purposes. The proposed technique specifically addresses the steady-state response, thus strongly reducing the computational burden associated with the neural network training stage and generating deep learning models with fewer parameters than similar architectures considering generic time-dependent problems. The approach is validated on a disk resonating gyroscope exhibiting auto-parametric resonance.

Automated summary

We propose a deep learning-based reduced order modelling approach for micro-electromechanical systems, which can treat fully coupled electromechanical problems in a non-intrusive way and provide real-time solutions across the whole device domain. This technique specifically addresses the steady-state response, reducing the computational burden and generating models with fewer parameters. The approach is validated on a disk resonating gyroscope exhibiting auto-parametric resonance.