An Adaptive Framework for Uncertainty Quantification in the Material Point Method

This study presents an efficient Monte Carlo simulation method to solve multivariate uncertainty problems in structural dynamic systems. The full model is obtained by the adaptive time-step material point method, and the multivariate surrogate model is built and analyzed for uncertainty by the adaptive sparse polynomial chaos expansion. The cubic spline interpolation method serves as a bridge between the original full model and the multivariate surrogate model, which improves the sampling efficiency of Monte Carlo simulation. Numerical results show that the proposed algorithm can significantly improve the efficiency and accuracy of uncertainty analysis.