从一般化的朗格文动态来测量长寿命内存内核的深度学习方法
Max Kerr Winter1, Ilian Pihlajamaa1, Vincent E Debets1
1Department of Applied Physics, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands.
The Journal of chemical physics
|June 27, 2023
概括
深度神经网络 (DNN) 现在可以测量复杂物理系统中的内存内核. 这种新的方法可以准确地从动态数据中提取内存效应,即使在杂的玻璃成型系统中也是如此.
科学领域:
- 物理 物理学 物理
- 复杂的系统复杂的系统.
- 机器学习 机器学习
背景情况:
- 记忆效应在各种物理现象中至关重要,如玻璃动力学和气候模型.
- 通用朗格温方程 (GLE) 描述了这些效应使用内存内核,但它往往是未知的和难以测量的.
- 目前用于内核预测的现有方法是计算密集型的,并且与杂数据作斗争.
研究的目的:
- 开发一种新的基于深度神经网络 (DNN) 的方法,用于从动态数据中测量内存内核.
- 解决系统中未知内存内核的挑战,这些内存内核是由通用朗格温方程 (GLE) 描述的.
- 为各种物理系统展示DNN方法的稳定性和通用性.
主要方法:
- 利用深度神经网络 (DNN) 来学习从动态数据到内存内核的映射.
- 训练网络使用模态合理论 (MCT) 为硬球生成的数据.
- 在不同的系统上测试了概括,包括布朗的维克斯-钱德勒-安德森粒子和现象核.
主要成果:
- DNN 准确地测量内存内核,即使在有噪音数据的情况下也有效.
- 与传统技术相比,该方法表现出了显著的稳定性.
- 在理论数据上训练的网络可以很好地将不同系统的模拟数据泛化.
结论:
- 深度学习为从动态数据中提取内存内核提供了一种强大而稳健的方法.
- 该KernelLearner管道为分析非马科夫系统提供了一个通用的解决方案.
- 这项工作突出了DNN在研究具有记忆效应的复杂动态系统方面的潜力.
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