Discovery of sparse hysteresis models for piezoelectric materials

نویسندگان

چکیده

This article presents an approach for modeling hysteresis in piezoelectric materials that leverages recent advancements machine learning, particularly sparse-regression techniques. While sparse regression has previously been used to model various scientific and engineering phenomena, its application nonlinear yet be explored. The study employs the least squares algorithm with a sequential threshold dynamic system responsible hysteresis, resulting concise accurately predicts both simulated experimental material data. Several numerical experiments are performed, including learning butterfly shaped real-world data actuator. presented is compared traditional regression-based neural network methods, demonstrating efficiency robustness.

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ژورنال

عنوان ژورنال: Applied Physics Letters

سال: 2023

ISSN: ['1520-8842', '0003-6951', '1077-3118']

DOI: https://doi.org/10.1063/5.0146134