Uncertainty modeling is a methodological approach and academic concept focused on identifying, quantifying, and representing the various sources and types of uncertainty present in data, models, and complex systems. This field investigates the origination and propagation of uncertainties through computational or analytical frameworks and their impact on predicted outcomes. Key characteristics involve employing statistical, probabilistic, or non-probabilistic formalisms to characterize variability, imprecision, and lack of knowledge. Its significance lies in enabling robust decision-making, risk assessment, and reliability analysis in domains where precise knowledge is limited, providing a structured basis for managing the inherent unpredictability in complex phenomena like physical processes or engineered systems.