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基于长短期记忆网络的纳米晶合金歇斯底里性能预测的研究

Hailin Li¹, Bo Zhang², Yongpeng Shen¹

¹College of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450000, China.

Scientific reports

|February 24, 2025

概括

这项研究引入了一种新的数据驱动模型,使用神经网络来预测各种频率的纳米晶合金中的磁性歇斯底里. 该模型准确地模拟了hysteresis特性,提供了一种新的模拟方法.

科学领域:

材料科学材料科学材料科学
计算物理计算物理
人工智能的人工智能

背景情况:

纳米晶体合金中的磁性歇斯底里是复杂的,并且取决于频率.
准确预测hysteresis对于材料表征和设备设计至关重要.
对于B-H数据的传统测量方法是耗时的.

研究的目的:

开发一个基于数据的模型,用于预测不同频率的纳米晶合金的磁性歇斯底里特征.
利用人工神经网络来捕捉复杂的非线性磁性行为.
为实验性B-H数据采集提供一种高效的替代方案.

主要方法:

使用了一个编码器-解码器架构,该架构结合了长短期记忆 (LSTM) 网络和前神经网络 (FNN).
吉尔斯-阿瑟顿 (J-A) 模型是使用有限的B-H测量数据来生成培训和验证集来识别的.
建议的神经网络模型使用生成的数据进行了训练和验证.

主要成果:

数据驱动模型成功地预测了考虑到频率效应的磁性歇斯底里特征.
对单独数据集的验证证明了模型的准确性,最大误差约为10.29%.
该模型有效地学习了纳米晶体合金的非线性磁性歇斯底里行为.

结论:

提出的基于神经网络的hysteresis模型为模拟频率依赖的磁性hysteresis提供了一种可行且准确的方法.
这种方法为分析和模拟纳米晶合金磁性质提供了一个新的计算工具.
该研究强调了材料科学中数据驱动方法在复杂性质预测方面的潜力.

关键词:

数据驱动的数据驱动.编码器解码器架构编码器解码器长期短期内存网络中的长期内存.磁性歇斯底里的特点是磁性歇斯底里.

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