最佳的参数化分组用于预测临界点和更高阶的关键过渡
Mickaël D Chekroun1, Honghu Liu2, James C McWilliams3
1Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, California 90095-1565, USA and Department of Earth and Planetary Sciences, Weizmann Institute of Science, Rehovot 76100, Israel.
Chaos (Woodbury, N.Y.)
|September 20, 2023
概括
本研究介绍了一种最佳参数化多重体 (OPM) 方法,用于从复杂系统创建缩小模型. 该方法通过优化通过数据知情方法进行参数化,准确地预测关键转换和倾斜现象.
科学领域:
- 动态系统理论 动态系统理论
- 计算建模 计算建模
- 数据驱动科学数据驱动科学
背景情况:
- 经典的减少顺序建模依赖于不变或缓慢的多元体.
- 这是一个很棒的节目,这是一个很棒的节目.
研究的目的:
- 提出一种一般的,可变的方法,用于从非自主系统中推导低阶缩小模型.
- 引入最佳参数集群 (OPM) 作为继任者不变/缓慢集群在奴役崩时.
- 通过使用缩小模型,能够准确预测关键转换和倾斜现象.
主要方法:
- 引入了最佳参数化多元组 (OPM) 概念,定义了一个多元组,该多元组以已解决的变量为条件的未解决变量的最佳平均值.
- 参数化来自于在不稳定性开始附近有效的持续变形,使用辅助向后向前系统.
- 分析参数化公式是通过优化向后集成时间来获得的,以数据为指导的最小化参数化缺陷为指导.
主要成果:
- OPM方法成功地导出了能够预测更高阶关键转换和灾难性倾斜现象的缩小系统.
- 优化每个尺度/变量的向后集成时间允许准确的参数化,即使在混乱的制度中,经典方法也会失败.
- 在过渡前的训练中训练有素的模型对随后的关键事件显示出更高的预测准确性.
结论:
- OPM框架提供了一种可靠的方法,用于从复杂的非自主系统中开发精确的减少顺序模型.
- 这种多变的方法克服了传统多元概念的局限性,特别是当"奴役"崩时.
- 由此衍生而来的简化系统为理解和预测动态系统的突然变化提供了有价值的工具.
相关概念视频
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