  In this paper we present a data-driven approach for uncertainty propagation.
In particular, we consider stochastic differential equations with parametric
uncertainty. Solution of the differential equation is approximated using
maximum entropy (maxent) basis functions similar to polynomial chaos
expansions. Maxent basis functions are derived from available data by
maximization of information-theoretic entropy, therefore, there is no need to
specify basis functions beforehand. We compare the proposed maxent based
approach with existing methods.
