Researchers Examine Uncertainty Quantification in Machine Learning for Dynamical Systems
Researchers have published a preprint on arXiv arguing that uncertainty modeling for dynamical systems requires a more tailored framework than what is typically used in supervised machine learning. The paper distinguishes between aleatoric uncertainty (inherent randomness) and epistemic uncertainty (knowledge gaps), examining how each arises in dynamical settings. The work matters because dynamical systems—such as physical simulations, control systems, and time-series models—present unique uncertainty challenges that existing ML frameworks may not adequately address.
A preprint submitted to arXiv on June 10, 2026, and presented at the EIML workshop at ICML, asks what kinds of uncertainty are necessary and sufficient for machine learning models applied to dynamical systems. The authors, led by Yusuf Sale, draw on the established distinction between aleatoric uncertainty (irreducible randomness in the data-generating process) and epistemic uncertainty (reducible uncertainty stemming from limited knowledge or data). While this distinction has been extensively studied in supervised learning and generative modeling, the paper argues it has received comparatively little attention in the context of dynamical systems. The authors systematically discuss the sources of uncertainty specific to dynamical settings and examine how the goals of uncertainty representation and quantification shift depending on the downstream task. The paper aims to provide a clearer conceptual foundation for researchers building probabilistic models of time-evolving systems.
What's missing
As a workshop paper (EIML@ICML) rather than a full peer-reviewed publication, the work has not yet undergone formal review. The abstract does not specify which dynamical system domains (e.g., physical systems, robotics, climate modeling) are covered, nor whether empirical experiments are included or the contribution is primarily conceptual. The paper's specific conclusions and proposed frameworks cannot be assessed from the abstract alone.
What different sources said
- arXiv cs.LGCenter
What Uncertainties Do We Need for Dynamical Systems?
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