CHARACTERIZATION OF PIEZOELECTRIC ACTUATOR WITH PHYSICS-INFORMED NEURAL NETWORKS

被引：0

作者：

Nguyen, Binh H. ^{[1
]}

Torri, Guilherme B. ^{[1
]}

Rochus, Veronique ^{[1
]}

机构：

[1] Imec, Sensor & Actuator Technol Dept, B-3001 Leuven, Belgium

来源：

2024 IEEE 37TH INTERNATIONAL CONFERENCE ON MICRO ELECTRO MECHANICAL SYSTEMS, MEMS | 2024年

关键词：

Physics-informed neural networks; Piezoelectric actuator; PZT; MEMS;

D O I：

10.1109/MEMS58180.2024.10439437

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：505 / 508

页数：4

共 6 条

[1] Machine Learning for Fluid Mechanics [J].

Brunton, Steven L. ;

Noack, Bernd R. ;

Koumoutsakos, Petros .

ANNUAL REVIEW OF FLUID MECHANICS, VOL 52, 2020, 52 :477-508

[2] Deep learning for non-parameterized MEMS structural design [J].

Guo, Ruiqi ;

Sui, Fanping ;

Yue, Wei ;

Wang, Zekai ;

Pala, Sedat ;

Li, Kunying ;

Xu, Renxiao ;

Lin, Liwei .

MICROSYSTEMS & NANOENGINEERING, 2022, 8 (01)

[3] A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics [J].

Haghighat, Ehsan ;

Raissi, Maziar ;

Moure, Adrian ;

Gomez, Hector ;

Juanes, Ruben .

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 2021, 379

[4] Physics-informed machine learning [J].

Karniadakis, George Em ;

Kevrekidis, Ioannis G. ;

Lu, Lu ;

Perdikaris, Paris ;

Wang, Sifan ;

Yang, Liu .

NATURE REVIEWS PHYSICS, 2021, 3 (06) :422-440

[5] Modeling of ferroelectric micro-cantilever actuator [J].

Nguyen, B. H. ;

Czarnecki, P. ;

Zunic, M. ;

Torri, G. B. ;

Rochus, V. .

2023 SYMPOSIUM ON DESIGN, TEST, INTEGRATION & PACKAGING OF MEMS/MOEMS, DTIP, 2023,

[6] Deep learning model as an inversion tool for resonant ultrasound spectroscopy of piezoelectric materials [J].

Yang, Wuyi ;

Sun, Shanshan ;

Hu, Jing ;

Tang, Liguo ;

Qin, Lei ;

Li, Zhenglin ;

Luo, Wenyu .

APPLIED PHYSICS LETTERS, 2022, 120 (18)

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