通过使用高效的机器学习来推断临界点并模拟复杂系统的非静态动态
Daniel Köglmayr1, Christoph Räth2
1German Aerospace Center (DLR), Institute for AI Safety and Security, 89081, Ulm, Germany. daniel.koeglmayr@dlr.de.
我们开发了一种数据驱动的机器学习算法,用于预测复杂系统中的临界点过渡. 这种方法预测系统行为,即使参数变化,模拟看不见的动态.
科学领域:
- 复杂系统科学 复杂系统科学
- 非线性动力学是一种非线性动力学.
- 机器学习 机器学习
背景情况:
- 在非线性动态系统中预测转折点过渡至关重要.
- 对于复杂的系统,需要无模型和数据驱动的方法.
- 现有的方法在推断分叉行为时面临挑战.
研究的目的:
- 提出一种全新的,完全基于数据的机器学习算法.
- 为了推断非线性动态系统的分叉行为.
- 预测具有时间变化的参数的非静态动态.
主要方法:
- 使用下一代储计算.
- 在静态数据样本上训练算法.
- 应用训练的架构来预测动态.
主要成果:
- 算法成功地推断了临界点过渡.
- 该方法预测了具有时间变化的分叉参数的非静止动态.
- 可以模拟看不见的参数区域的转折点后动态.
结论:
- 开发的水库计算算法为预测关键过渡提供了一个强大的工具.
- 这种数据驱动的方法促进了对复杂系统行为的理解和预测.
- 该方法可以模拟超出观测数据的未来系统状态.
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