Fluid sensing using microcantilevers: from physics-based modeling to deep learning

DOI10.1016/J.APM.2020.06.051zbMath1481.74592OpenAlexW3041266267MaRDI QIDQ821898

Mehdi Ghommem, Fehmi Najar, Vladimir Evgenievich Puzyrev

Publication date: 21 September 2021

Published in: Applied Mathematical Modelling (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.apm.2020.06.051

zbMATH Keywords

neural networksdeep learningmicrocantileversdensity and viscosity measurementsfluid sensorsqueeze-film damping

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Micromechanics of solids (74M25) Incompressible viscous fluids (76D99)

Related Items (2)

Deep learning for simultaneous measurements of pressure and temperature using arch resonators ⋮ Deep learning for gas sensing using MOFs coated weakly-coupled microbeams

Uses Software

• Adam

Cites Work

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• Strong nonlinear dynamics of MEMS and NEMS structures based on semi-analytical approaches
• Photothermal response of polymer microcantilever with metal coating in fluids
• Machine learning closures for model order reduction of thermal fluids
• Shock response of electrostatically coupled microbeams under the squeeze-film damping effect
• Multifidelity modeling and comparative analysis of electrically coupled microbeams under squeeze-film damping effect
• Deep Convolutional Framelets: A General Deep Learning Framework for Inverse Problems

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