What Uncertainties Do We Need for Dynamical Systems?
Yusuf Sale, Christopher Bülte, Felix Czaja, Joshua Stiller, Eyke Hüllermeier
Abstract
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.
Topics
Classified with taxonomy v2 on Wed, 2 Sept 2026.