Advances in Simulation-Based Uncertainty Quantification and Reliability Analysis

Abstract

The special collection on Advances in Simulation-Based Uncertainty Quantification and Reliability Analysis is available in the ASCE Library (https://ascelibrary.org/page/ajrua6/simulation _based_uncertainty_quantification_reliability_analysis). Simulations are increasingly being used in lieu of or to supplement physical testing in several major industries from civil structural analysis and design to the automotive, aircraft, and naval industries. Two critical aspects of simulation-based analysis and design are the rigorous quantification of uncertainty and the ability to rapidly and accurately assess reliability. Monte Carlo simulation is the most robust simulation-based approach for such problems and serves as a benchmark against which new methods can be compared. The well-known problem with Monte Carlo methods, especially for reliability assessment, is their large computational expense imposed by the requirement of running a very large number of simulations. In recent years, rapid advances that improve on classical Monte Carlo simulation, coupled with improvements in computational resources, have begun to usher in a new era of simulation-based uncertainty analysis such that modern challenges, e.g., uncertainty quantification (UQ) for very large (e.g., highdimensional, computationally intensive) and complex (e.g., strongly nonlinear, multicomponent, multiscale, multiphysics) systems and inverse problems, are becoming increasingly tractable with a reasonable number of simulations. Examples of new methods include Markov chain Monte Carlo (MCMC) approaches such as subset simulations, sparse-grid stochastic collocation methods, Bayesian nested sampling, variance reduction techniques (i.e., Latin hypercube, importance sampling), and new adaptive Monte Carlo and quasi-Monte Carlo methods. This special collection aims at exploring the latest methodological developments in simulation-based uncertainty quantification and reliability analysis. The genesis of this special collection was a series of minisymposia on the topic organized by the coeditors and colleagues held at the 2016 Probabilistic Mechanics and Reliability Conference, the 2nd International Conference on Uncertainty Quantification in Computational Sciences and Engineering, and the 12th International Conference on Structural Safety and Reliability. These minisymposia saw a total of 39 speakers addressing some of the most pressing issues in computational uncertainty quantification and reliability analysis. Among these presenters,