Exploitation of sensitivity derivatives via randomized quasi-Monte Carlo methods

Monte Carlo methods are now widely used in solving various computational fluid dynamics systems. This paper presents an improved scrambled quasi-Monte Carlo method for solving fluid dynamics applications. In our parallel implementation we use an independent scrambled quasirandom sequence for each processor. We explore the use of both scrambled quasi-Monte Carlo and variance reduction methods to improve the accuracy for Monte Carlo schemes. We also present theoretical analyses and numerical experiments to validate our numerical algorithms.