Simple probabilistic models such as random variables are often insufficient for describing uncertain input parameters. In cases where a spatial or temporal correlation exists, stochastic processes or fields are required to model such correlated uncertain parameters. Examples include spatially variable material or friction parameters, variations of component dimensions caused by inevitable manufacturing tolerances, or blurred geometries of biological structures due to insufficient resolution of medical imaging. Since variations of many physical parameters do not abide by a Gaussian distribution, non-Gaussian stochastic fields with more complex and skewed distributions are often required. AdCo EngineeringGW has developed a library for generating stochastic processes, which replicate such uncertainties accurately and efficiently in simulation models.