基于深度学习的洛伦茨系统与控制参数的状态预测
Xiaolong Wang1,2, Jing Feng3, Yong Xu2,4
1School of Mathematics and Statistics, Shaanxi Normal University, Xi'an 710119, China.
Chaos (Woodbury, N.Y.)
|March 5, 2024
概括
一个非常深层的神经网络准确地模拟非线性动态系统,学习固定点,周期和混乱的解决方案. 这种深度学习方法使得准确的长期预测和系统动态恢复成为可能.
科学领域:
- 计算物理学的计算物理.
- 人工智能的人工智能是人工智能.
- 非线性动力学是一种非线性动力学.
背景情况:
- 浅层神经网络难以建模复杂的非线性动态系统.
- 像洛伦兹系统这样的参数依赖系统在建模方面存在重大挑战.
研究的目的:
- 开发一种深度学习模型,能够同时学习非线性系统的多种解决方案 (固点,周期,混乱).
- 为了研究非常深层神经网络对参数依赖的洛伦茨系统建模的有效性.
主要方法:
- 采用了一个非常深层的神经网络架构,有许多相同的线性层.
- 结合了剩余的连接,以促进信息流动.
- 为了训练深度学习模型,使用了大量的数据集.
主要成果:
- 深度学习模型准确地预测了几次Lyapunov时间的混乱解决方案.
- 对于周期性解决方案,成功实现了长期预测.
- 动态特征,包括分叉图和最大的利亚普诺夫指数,得到了很好的恢复.
结论:
- 非常深层的神经网络为复杂的动态系统提供了卓越的非线性映射能力.
- 提出的深度学习方法使得准确的长期预测和非线性动态的特征.
- 这项研究强调了深度学习在促进混乱系统的理解和预测方面的潜力.
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