Researchers Examine Uncertainty Quantification in Machine Learning for Dynamical Systems
A new arXiv paper explores how to model and quantify uncertainty in dynamical systems using machine learning approaches. The work distinguishes between aleatoric uncertainty (inherent randomness) and epistemic uncertainty (knowledge gaps) in this context, which has received less research attention than uncertainty in supervised learning. Understanding these uncertainties is important for building more reliable machine learning systems that can represent the limitations of their predictions.
Researchers have published a paper on arXiv examining uncertainty quantification in machine learning models for dynamical systems. The work addresses a gap in the literature by applying the well-studied distinction between aleatoric and epistemic uncertainty—concepts developed primarily for supervised learning—to the less-explored domain of dynamical systems. The paper discusses the sources of uncertainty in this context, clarifies their nature, and examines how uncertainty representation and quantification objectives vary depending on the specific task at hand. This research contributes to the broader effort to make machine learning systems more transparent about their limitations and more reliable in applications where understanding prediction uncertainty is critical.
What different sources said
- arXiv cs.LGCenter
What Uncertainties Do We Need for Dynamical Systems?
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