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Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
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The stability of equilibrium configurations is an important concept in physics, engineering, and other related fields. In simple terms, it refers to the tendency of an object or system to return to its equilibrium position after being disturbed. The stability of an equilibrium configuration can be analyzed by considering the potential energy function of the system and examining its behavior near the equilibrium point.
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使用PDE受约束优化对不变指标的学习动态.

Jonah Botvinick-Greenhouse1, Robert Martin2, Yunan Yang3

  • 1Center for Applied Mathematics, Cornell University, Ithaca, New York 14850, USA.

Chaos (Woodbury, N.Y.)
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概括
此摘要是机器生成的。

我们开发了一种新的方法来从数据中学习连续时间动态系统,将其定义为PDE受约束的优化问题. 这种方法可以从稀疏的数据中学习,并为预测提供不确定性量化.

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科学领域:

  • 动态系统理论 动态系统理论
  • 科学机器学习科学机器学习
  • 计算物理 计算物理

背景情况:

  • 学习自主连续时间动态系统对于模拟复杂现象至关重要.
  • 现有的方法通常需要密集采样数据,这限制了它们的适用性.
  • 不变量提供了系统动态的强有力的统计描述.

研究的目的:

  • 扩展现有的从不变量测量学习动态系统的方法.
  • 重构学习普通微分方程 (ODEs) 或随机微分方程 (SDEs) 的反向问题作为一个受PDE约束的优化问题.
  • 为了使从缓慢采样的数据中学习,并执行不确定性量化.

主要方法:

  • 重构反向问题作为一个受PDE约束的优化.
  • 使用不变的措施来识别系统.
  • 开发一种具有增强稳定性质的前模型.

主要成果:

  • 成功地从不变量测量中学习了自主连续时间动态系统.
  • 从缓慢采样的推理轨迹中展示了有效的学习.
  • 实现了预测动态的不确定性量化.
  • 在特定情况下,与直接模拟相比,展现出更好的前模型稳定性.

结论:

  • 拟议的PDE受约束优化方法为学习动态系统提供了一个强大的和多功能框架.
  • 该方法对基准系统 (范德波尔,洛伦兹-63) 和现实应用 (霍尔效应推进器,温度预测) 都有效.
  • 这种方法提高了从有限的观测数据中建模和预测复杂动态的能力.