Efficient Uncertainty Quantification with Polynomial Chaos for Implicit Stiff Systems

The polynomial chaos method has been widely adopted as a compu-tationally feasible approach for uncertainty quantification. Most studies to date have focused on non-stiff systems. When stiff systems are consid-ered, implicit numerical integration requires the solution of a nonlinear system of equations at every time step. Using the Galerkin approach, the size of the system state increases from n to S × n, where S is the number of the polynomial chaos basis functions. Solving such systems with full linear algebra causes the computational cost to increase from O(n3) to O(S3n3). The S3-fold increase can make the computational cost prohibitive. This paper explores computationally efficient uncertainty quantification techniques for stiff systems using the Galerkin, collocation and collocation least-squares formulations of polynomial chaos. In the Galerkin approach, we propose a modification in the implicit time step-ping process using an approximation of the Jacobian matrix to reduce the computational cost. The numerical results show a run time reduction with a small impact on accuracy. In the stochastic collocation formu-lation, we propose a least-squares approach based on collocation at a low-discrepancy set of points. Numerical experiments illustrate that the collocation least-squares approach for uncertainty quantification has sim-ilar accuracy with the Galerkin approach, is more efficient, and does not require any modifications of the original code.