Uncertainty Quantification for High-dimensional Stochastic Processes and their Applications

In this mini-symposium we explore the construction, reduction, propagation, and applications of high-dimensional stochastic processes, e.g., random fields, to computational mechanics. Stochastic processes play an important role in characterizing and describing the inherent uncertainty in many applications to mechanics. For example, material properties such as diffusivity (flow through porous media), elasticity or stiffness (Young’s modulus) may be modeled via stochastic processes. More often than not these quantities can be very high dimensional, making them difficult to construct and model, both in a practical and mathematical sense. Thus, this mini-symposium is aimed at bringing researchers together to share algorithmic and computational techniques on working with such high dimensional processes.