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通过神经通缩来学习独立的保存定律的机器学习
Wei Zhu1, Hong-Kun Zhang1, P G Kevrekidis1
1Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, Massachusetts 01003-4515, USA.
我们开发了神经通缩,一种使用神经网络在动态系统中找到保存定律的新方法. 该工具有助于识别保存量并评估复杂模型中的潜在整合性.
科学领域:
- 动态系统 动态系统
- 计算物理 计算物理
- 机器学习 机器学习
背景情况:
- 哈密尔顿动态系统是物理学的基础.
- 识别保护规律对于理解系统的整合性至关重要.
- 现有的寻找保护规律的方法可能是计算密集的.
研究的目的:
- 引入一种新的数据驱动方法来发现保护规律.
- 用计算工具评估动态系统的可集成性.
- 将该方法应用于格子微分方程.
主要方法:
- 开发"神经通缩",一种代的神经网络训练方法.
- 规则化损失函数的最小化,强制执行保留数量的内置和功能独立性.
- 对于可整合和不可整合的格子微分方程的应用.
主要成果:
- 神经通缩成功地预测了各种系统中的保存规律.
- 对于可集成的系统,保存定律的数量随自由度的变化而变化.
- 对于不可整合的系统,发现保存规律在值时会和.
结论:
- 神经通缩是一种有效的工具,用于识别保护规律.
- 该方法提供了关于动态系统可集成性的见解.
- 这种数据驱动的方法有助于模型评估和理论分析.
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