Deep learning for nonlinear characterization of electrostatic vibrating beam MEMS

DOI10.1142/s0218127423300380zbMATH Open1546.78014MaRDI QIDQ6539003

Vladimir Evgenievich Puzyrev, Basil Alattar, Mehdi Ghommem

Publication date: 14 May 2024

Published in: International Journal of Bifurcation and Chaos in Applied Sciences and Engineering (Search for Journal in Brave)

zbMATH Keywords

hysteresisbifurcationnonlinear dynamicsdeep learningmotion-induced current

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Neural and heuristic methods applied to problems in optics and electromagnetic theory (78M32)

Cites Work

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• Fluid sensing using microcantilevers: from physics-based modeling to deep learning
• Deep learning for simultaneous measurements of pressure and temperature using arch resonators
• Deep learning for gas sensing using MOFs coated weakly-coupled microbeams
• DYNAMICS OF ELECTROSTATICALLY ACTUATED MICRO-ELECTRO-MECHANICAL SYSTEMS: SINGLE DEVICE AND ARRAYS OF DEVICES

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