厘清动态系统中需建模的不确定性类型及其用途
What Uncertainties Do We Need for Dynamical Systems?
- 区分动态系统中的随机性与认知性不确定性
- 明确不同任务下不确定性建模的目标差异
- 为时序建模提供可解释的不确定性分析框架
机器学习中对随机性与认知性不确定性的区分已受到广泛关注，主要集中在监督学习，但也涉及生成建模等场景。本文从机器学习视角探讨动态系统的不确定性建模问题，该领域研究仍相对不足。我们提出核心问题：动态系统需要哪些不确定性？通过分析不确定性来源，澄清其性质（随机或认知），并考察不同任务中不确定性表征与量化目标的差异。
原文摘要 · Abstract (English)
The distinction between aleatoric and epistemic uncertainty has received considerable attention in machine learning research, mainly in the context of supervised learning but also in other settings such as generative modeling. In this paper, we offer a machine learning perspective on uncertainty modeling for dynamical systems, which has been studied much less so far. In particular, we ask: what uncertainties do we need for dynamical systems? We discuss sources of uncertainty, clarify their nature (aleatoric or epistemic), and consider how the objectives of representing and quantifying uncertainty vary across different tasks.
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