Editorial: Uncertainty quantification in characterization, modelling, and design of materials

Uncertainties arise from a variety of sources, including experimental/measurement errors, imperfections in manufacturing/processing, modelling and computational assumptions, lack of knowledge associated with numerical models, and model-form errors. Therefore, traditional deterministic approaches in the characterization, modelling, and design of materials often fall short of achieving trustworthy and reliable predictions of the properties and performance of materials. Several problemsmay occur due to the use of these traditional deterministic techniques since the uncertainty in material behaviour may cause undesirable deviations in expected material properties, which could alter the performance of structures, or even cause the failure of critical systems. As a result, characterization, modelling, and design of materials require the consideration of the influence of uncertainties on material behaviour and properties to improve the performance and reliability of structures and critical systems. Recent advances in this area include the development of theoretical, numerical, data-driven, and machinelearning-reinforced uncertainty quantification methods for applications in materials science and engineering problems across multiple length scales and time scales. This Research Topic has covered the advances in the development and application of uncertainty quantification techniques for investigating the effects of uncertainties on physical material behaviour and designing materials under uncertainty. The published studies have covered topics ranging from crystal plasticity and fatigue nucleation modelling of materials by considering the effects of material uncertainties to the OPEN ACCESS