Computational modeling and design require decision-making in the presence of uncertainties due to variations in material and geometric parameters, external loads, and environmental conditions. In general, data and information are characterized by aleatory uncertainty (natural variability) and epistemic uncertainty (lack of knowledge, and imprecision). For a realistic risk and structural safety assessment, both types of uncertainty characteristics have to be considered using probabilistic (random variables) and non-traditional methods (e.g., interval /fuzzy variables, and polymorphic / hybrid / mixed uncertainty models). The numerical implementation requires a computationally expensive nested loop algorithm. Therefore, the computaional design of complex structures accounting for uncertainties is quite challenging and needs ongoing research. This minisymposium addresses recent developments and current challenges in uncertainty quantification in computational mechanics. Areas of interest include, but are not limited to: • Uncertainty quantification using probabilistic approaches (random variables) and non-traditional methods (e.g., interval /fuzzy variables, and polymorphic / hybrid / mixed uncertainty models) • Random fields and processes • Stochastic finite element methods • Structural optimization considering uncertainties • Resilience-based design • Sensitivity analysis • Risk analysis • Surrogate modeling strategies • Sampling methods • AI methods and high-performance computing for UQ • Applications of UQ, e.g., in solid mechanics, material modeling, stability analysis, structural dynamics, multi-scale and multi-physics simulation