关于学习学习什么:对动态的异质观测,并建立它们之间可能的因果关系
David W Sroczynski1, Felix Dietrich2, Eleni D Koronaki3
1Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544, USA.
PNAS nexus
|December 11, 2024
概括
本研究提出了基于数据的方法,以从物理过程观察中确定学习函数的相关输入和输出. 它区分了常见的可观测值和过程特定的信息,以实现准确的函数学习.
科学领域:
- 数据驱动的建模.
- 物理系统分析物理系统分析.
- 处理观测数据的数据处理.
背景情况:
- 从物理过程中学习函数需要定义输入-输出关系.
- 来自多个观测过程的同时异质数据流在科学观测中很常见.
研究的目的:
- 展示两种数据驱动的方法来选择适当的可观察值 (输入和输出) 用于函数学习.
- 处理观测数据流,在不同的测量过程中识别共同和独特的信息.
主要方法:
- 处理来自物理系统观测的同时异质数据流 (时间序列集合).
- 在不同的观测过程中确定可观测的共同子集.
- 识别与常见可观测物无关的信息,特定于每个观测过程.
主要成果:
- 识别了可以通过函数联系在一起的共同可观察值.
- 显著的过程特定信息不对所需的功能有所贡献.
- 展示了两种方法:将单个观测结果映射到整个水平集,并将当前与未来测量的关系用于动态建模.
结论:
- 开发的方法通过正确定义输入-输出关系,使得可靠的数据驱动函数学习成为可能.
- 该框架支持高级技术,如高斯过程和神经网络用于建模.
- 该方法为测量识别提供了洞察力,并为因果推理和动态系统建模提供了基础.
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