Characterization Of Piezoelectric Actuator With Physics-Informed Neural Networks
- Resource Type
- Conference
- Authors
- Nguyen, Binh H.; Torri, Guilherme B.; Rochus, Veronique
- Source
- 2024 IEEE 37th International Conference on Micro Electro Mechanical Systems (MEMS) Micro Electro Mechanical Systems (MEMS), 2024 IEEE 37th International Conference on. :505-508 Jan, 2024
- Subject
- Bioengineering
Components, Circuits, Devices and Systems
Fields, Waves and Electromagnetics
Photonics and Electrooptics
Power, Energy and Industry Applications
Micromechanical devices
Neural networks
Piezoelectric actuators
Predictive models
Loss measurement
Data models
Synthetic data
Physics-informed neural networks
Piezoelectric actuator
PZT
MEMS
- Language
- ISSN
- 2160-1968
This paper introduces for the first time the application of physics-informed neural networks (PINNs) in characterizing microelectromechanical systems (MEMS), particularly for piezoelectric micro-actuator. The capability of the constructed PINNs model is demonstrated through inversely determining piezoelectric coefficient from measured deformation data of an electrically actuated beam. This work serves as a starting point of the merging between MEMS design and machine learning techniques.