Wilkins Aquino, Duke University, A Locally Adapted Reduced Basis Method for Solving Risk-Averse PDE-Constrained Optimization Problems
SAS 4201The numerical solution of large-scale risk-averse PDE-constrained optimization problems requires substantial computational effort due to the discretization in physical and stochastic dimensions. Managing the cost is essential to tackle such problems with high dimensional uncertainties. In this work, we combine an inexact trust-region (TR) algorithm from with a local, reduced basis (RB) approximation to efficiently solve risk-averse optimization problems…