What Uncertainties Do We Need for Dynamical Systems?
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 54 references that are in the map. First posted on arXiv 10 Jun 2026.
Builds on
6 of 54 references.
References · 6
- 2022FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operatorscited by 776
- 2019Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limitcited by 176
- 2015Deep Kalman Filterscited by 166
- 2018Stable Recurrent Modelscited by 66
- 2020Bayesian Neural Ordinary Differential Equationscited by 45
- 2023Sources of Uncertainty in Supervised Machine Learning -- A Statisticians' Viewcited by 28
Built on by
Not cited yet.