Managing Computational Complexity: UQ with Simulation Models of Different Fidelities

Monday, December 16, 2013 - 2:30pm - 3:00pm
Lind 305
Dongbin Xiu (The University of Utah)
UQ computations can be highly time consuming. When the underlying deterministic systems are very large, high fidelity UQ simulations can be prohibitively expensive, if not impossible. However, in many practical problems, there often exist a set of models, each with different fidelities or accuracy for the problem. Typically, high fidelity models are highly accurate but expensive to run; low fidelity models are, albeit not highly accurate, able to capture important features of the problem and cheap to simulate. It is therefore desirable to take advantage of the existence of all these models and construct a practical UQ algorithm to conduct reliable and accurate UQ analysis with reasonable simulation cost.

In this talk, we present a newly developed UQ algorithm that accomplishes this goal. It utilizes both the speed of the low-fidelity models and the accuracy of the high-fidelity models, and is able to provide accurate UQ results. Furthermore, the algorithm is rigorous, as its numerical error bound has been established. We will present both the mathematical framework and implementation details, and then illustrate its efficacy via a set of examples.