压电材料稀疏迟滞模型的发现
Discovery of sparse hysteresis models for piezoelectric materials
AI总结：
本文提出利用带序贯阈值的最小二乘稀疏回归算法建模压电材料的非线性迟滞现象，该方法在预测精度上表现优异且比传统方法更高效鲁棒。
摘要：
本文提出了一种利用机器学习（特别是稀疏回归技术）最新进展来建模压电材料中迟滞现象的方法。尽管稀疏回归此前已被用于建模各种科学和工程现象，但其在压电材料非线性迟滞建模中的应用仍有待探索。本研究采用带序贯阈值的最小二乘算法对产生迟滞的动态系统进行建模，从而得到一个简洁的模型，能够准确预测模拟和实验压电材料数据的迟滞现象。研究进行了多项数值实验，包括学习蝴蝶形迟滞以及对压电致动器的真实迟滞数据进行建模。该方法与传统的基于回归和神经网络的方法进行了比较，证明了其高效性和鲁棒性。源代码可在 https://github.com/chandratue/SmartHysteresis 获取。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
