Analysis of a printed circuit board with many uncertain variables by sparse polynomial chaos

This communication deals with the uncertainty quantification in high dimensional problems. It introduces a metamodel based on the sparse polynomial chaos for the analysis of a printed circuit board, depending on many uncertain variables. This metamodel allows to estimate statistical quantities of an output with a relative low computational cost compared to Monte Carlo (MC) simulation. Results obtained have been validated by comparison with MC simulation.