Discovery of sparse hysteresis models for piezoelectric materials

• URL: http://arxiv.org/abs/2302.05313v5
• Date: Mon, 15 May 2023 14:29:59 GMT
• Title: Discovery of sparse hysteresis models for piezoelectric materials
• Authors: Abhishek Chandra, Bram Daniels, Mitrofan Curti, Koen Tiels, Elena A. Lomonova and Daniel M. Tartakovsky
• Abstract summary: This article presents an approach for modelling in piezoelectric materials using sparse-regression techniques. The presented approach is compared to traditional regression-based and neural network methods, demonstrating its efficiency and robustness.
• Score: 1.3669389861593737
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This article presents an approach for modelling hysteresis in piezoelectric materials, that leverages recent advancements in machine learning, particularly in sparse-regression techniques. While sparse regression has previously been used to model various scientific and engineering phenomena, its application to nonlinear hysteresis modelling in piezoelectric materials has yet to be explored. The study employs the least-squares algorithm with a sequential threshold to model the dynamic system responsible for hysteresis, resulting in a concise model that accurately predicts hysteresis for both simulated and experimental piezoelectric material data. Several numerical experiments are performed, including learning butterfly-shaped hysteresis and modelling real-world hysteresis data for a piezoelectric actuator. The presented approach is compared to traditional regression-based and neural network methods, demonstrating its efficiency and robustness. Source code is available at https://github.com/chandratue/SmartHysteresis

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