Uncertainty quantification and model validation of fatigue crack growth prediction

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Uncertainty quantification and management in additive manufacturing: current status, needs, and opportunities (2017, background): “4, epistemic uncertainty can be further divided into two groups, namely data uncertainty and model uncertainty [92].”
Simulator-free solution of high-dimensional stochastic elliptic partial differential equations using deep neural networks (2019, background): “…has been applied with great success on a very diverse set of applications with moderate stochastic dimensionality, such as fracture mechanics [11], biological systems [12], molecular dynamics [13], nuclear engineering [14], etc. Traditional choices for surrogate models include Gaussian…”
A comprehensive review of digital twin — part 1: modeling and twinning enabling technologies (2022, background): “Examples of semi-empirical models include the Paris’ law equation describing fatigue crack growth widely used for fatigue damage prognostics (Sankararaman et al. 2011) and the square-root-of-time dependence capturing battery capacity fade due to the solid electrolyte interphase growth for battery li”