This tutorial paper reviews the use of advanced Monte Carlo sampling methods in the
context of Bayesian model updating for engineering applications. Markov Chain Monte …

This work addresses a comprehensive review of the recent efforts for uncertainty
quantification in small-scale materials science. Experimental and computational studies for …

The nonlinear, history-dependent macroscopic behavior of a granular material is rooted in
the micromechanics between constituent particles and irreversible, plastic deformations …

We present Π4U, 1 an extensible framework, for non-intrusive Bayesian Uncertainty
Quantification and Propagation (UQ+ P) of complex and computationally demanding …

This paper applies the transitional Markov chain Monte Carlo (TMCMC) algorithm to
probabilistic site characterization problems. The purpose is to characterize the statistical …

Purpose Objective uncertainty quantification (UQ) of a product life-cycle assessment (LCA)
is a critical step for decision-making. Environmental impacts can be measured directly or by …

Hyperelastic constitutive models of soft tissue mechanical behavior are extensively used in
applications like computer-aided surgery, injury modeling, etc. While numerous constitutive …

The differential evolution (DE) algorithm was deployed to calibrate microparameters of the
DEM cohesive granular material. 4 macroparameters, namely, uniaxial compressive …

Abstract The transitional Markov chain Monte Carlo (TMCMC) is one of the efficient
algorithms for performing Markov chain Monte Carlo (MCMC) in the context of Bayesian …

The calibration of discrete element method (DEM) simulations is typically accomplished in a
trial-and-error manner. It generally lacks objectivity and is filled with uncertainties. To deal …