  Over the last few years there have been dramatic advances in our
understanding of mathematical and computational models of complex systems in
the presence of uncertainty. This has led to a growth in the area of
uncertainty quantification as well as the need to develop efficient, scalable,
stable and convergent computational methods for solving differential equations
with random inputs. Stochastic Galerkin methods based on polynomial chaos
expansions have shown superiority to other non-sampling and many sampling
techniques. However, for complicated governing equations numerical
implementations of stochastic Galerkin methods can become non-trivial. On the
other hand, Monte Carlo and other traditional sampling methods, are
straightforward to implement. However, they do not offer as fast convergence
rates as stochastic Galerkin. Other numerical approaches are the stochastic
collocation (SC) methods, which inherit both, the ease of implementation of
Monte Carlo and the robustness of stochastic Galerkin to a great deal. In this
work we propose a novel enhancement to stochastic collocation methods using
deterministic model reduction techniques. Linear parabolic partial differential
equations with random forcing terms are analysed. The input data are assumed to
be represented by a finite number of random variables. A rigorous convergence
analysis, supported by numerical results, shows that the proposed technique is
not only reliable and robust but also efficient.
