NAFeb 26, 2015
Projected Multilevel Monte Carlo Method for PDE with random input dataMyoungnyoun Kim, Imbo Sim
The order of convergence of the Monte Carlo method is 1/2 which means that we need quadruple samples to decrease the error in half in the numerical simulation. Multilevel Monte Carlo methods reach the same order of error by spending less computational time than the Monte Carlo method. To reduce the computational complexity further, we introduce a projected multilevel Monte Carlo method. Numerical experiments validate our theoretical results.
NAJun 9, 2015
Reduced Basis Method for the Convected Helmholtz EquationMyoungnyoun Kim, Imbo Sim
We present a reduced basis approach to solve the convected Helmholtz equation with several physical parameters. Physical parameters characterize the aeroacoustic wave propagation in terms of the wave and Mach numbers. We compute solutions for various combinations of parameters and spend a lot of time to figure out the desired set of parameters. The reduced basis method saves the computational effort by using the Galerkin projection, a posteriori error estimator, and greedy algorithm. Here, we propose an efficient a posteriori error estimator based on the primal norm. Numerical experiments demonstrate the good performance and effectivity of the proposed error estimator.