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.)
|June 27, 2023
概括
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.
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.
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