发现萨加索姆浮游艇质量中心的动态
1Department of Atmospheric Sciences, Rosenstiel School of Marine, Atmospheric & Earth Science, University of Miami, Miami, Florida 33149, USA.
Chaos (Woodbury, N.Y.)
|January 27, 2026
概括
机器学习模型,包括长短期记忆 (LSTM) 和稀疏识别非线性动力学 (SINDy) 已开发,以预测萨尔加索姆浮游艇的运动. 这些模型看起来很有前途,但在复杂的场景中扎,例如风力效应和松散连接的.
科学领域:
- 环境动力学环境动力学
- 计算流体动力学的流体动力学.
- 机器学习应用 机器学习应用
背景情况:
- 自2011年以来,漂浮的Sargassum海藻已经阻碍了美洲内海的海岸.
- 这些的运动是由复杂的eBOMB动态系统描述的.
- 对于萨尔加索姆浮游艇质量中心的闭合形式运动定律目前尚未确定.
研究的目的:
- 开发低维模型来预测萨尔加索浮游艇的运动.
- 评估和对比长期短期记忆 (LSTM) 和稀疏识别非线性动力学 (SINDy) 机器学习方法的性能.
- 为了提高数字效率和对萨尔加索姆浮游艇动态的概念理解.
主要方法:
- 物理启发的闭包建模植根于eBOMB模型.
- LSTM循环神经网络 (RNN) 经过训练,可以将eBOMB变量映射到速度差异.
- 使用eBOMB变量和候选函数库的窗口速度术语的SINDy模型.
主要成果:
- 两种LSTM和SINDy模型的性能都类似,对于紧密连接的木,精度更高.
- 在复杂的场景中,模型精度下降,包括风力影响和松散连接的.
- 简单设计的LSTM模型更有效,但缺乏可解释性;SINDy确定了明确的功能依赖.
结论:
- 机器学习模型提供了一个可行的方法来建模萨加索姆浮游艇动态.
- 整合窗式速度术语改善了非局部相互作用的建模,特别是在稀疏连接的上.
- 需要进一步开发,以改善在复杂环境条件下模型的准确性.
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