Sai Hung Cheung

TL;DR: In this paper, the authors investigated the feasibility of the Hybrid Monte Carlo method to solve higher-dimensional Bayesian model updating problems and proposed a new formulae for Markov chain convergence assessment.

TL;DR: Bayesian uncertainty quantification techniques are applied to the analysis of the Spalart–Allmaras turbulence model in the context of incompressible, boundary layer flows and it is shown that by using both the model plausibility and predicted QoI, one has the opportunity to reject some model classes after calibration, before subjecting the remaining classes to additional validation challenges.

TL;DR: A general method for calculating the evidence for each model class based on the system data, which requires the evaluation of a multi‐dimensional integral involving the product of the likelihood and prior defined by the model class.

TL;DR: Implementation of surrogate modeling, in particular moving least squares response surface methodologies, is suggested for efficient approximation of the model response for reduction of the computational burden associated with the stochastic sampling.

TL;DR: Evaluating two reliability methods recently proposed by the authors, referred to as spherical subset simulation (S3) and auxiliary domain method (ADM), show that both S3 and ADM are efficient for treating high dimensional reliability problems.