Neural networks based surrogate modeling for efficient uncertainty quantification and calibration of MEMS accelerometers-Reference-Cited by-同舟云学术

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Neural networks based surrogate modeling for efficient uncertainty quantification and calibration of MEMS accelerometers

Published:2024-09 Issue: Volume: Page:104902
ISSN:0020-7462
Container-title:International Journal of Non-Linear Mechanics
language:en
Short-container-title:International Journal of Non-Linear Mechanics

Author:

Zacchei Filippo^ORCID,Rizzini Francesco,Gattere Gabriele,Frangi Attilio,Manzoni Andrea

Funder

STMicroelectronics SA

Ministero dell'Istruzione dell'Università e della Ricerca

Publisher

Elsevier BV

Reference74 articles.

1. Mechanics of Microsystems;Corigliano,2018

2. Microelectromechanical systems (MEMS): fabrication, design and applications;Judy;Smart Mater. Struct.,2001

3. Mechanical characterization of polysilicon MEMS: A hybrid TMCMC/POD-Kriging Approach;Mirzazadeh;Sensors,2018

4. A laterally driven symmetric micro-resonator for gyroscopic applications;Hong;J. Micromech. Microeng.,2000

5. Characterization and calibration of MEMS inertial sensors for state and parameter estimation applications;Aydemir;Measurement,2012

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