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.

翻译：在机器学习研究中，偶然不确定性与认知不确定性的区分已受到广泛关注，主要是在监督学习背景下，但也涉及生成建模等其他场景。本文从机器学习视角探讨动态系统的不确定性建模，目前这一领域的研究相对较少。具体而言，我们提出：动态系统需要哪些不确定性？我们讨论了不确定性的来源，阐明了其本质（偶然性或认知性），并考虑了在不同任务中表示与量化不确定性的目标如何变化。