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Full Description
Uncertainty Quantification in Multiscale Materials Modeling provides a complete overview of uncertainty quantification (UQ) in computational materials science. It provides practical tools and methods along with examples of their application to problems in materials modeling. UQ methods are applied to various multiscale models ranging from the nanoscale to macroscale. This book presents a thorough synthesis of the state-of-the-art in UQ methods for materials modeling, including Bayesian inference, surrogate modeling, random fields, interval analysis, and sensitivity analysis, providing insight into the unique characteristics of models framed at each scale, as well as common issues in modeling across scales.
Contents
Uncertainty quantification in materials modeling
The uncertainty pyramid for electronic-structure methods
Bayesian error estimation in density functional theory
Uncertainty quantification of solute transport coefficients
Data-driven acceleration of first-principles saddle point and local minimum search based on scalable Gaussian processes
Bayesian calibration of force fields for molecular simulations
Reliable molecular dynamics simulations for intrusive uncertainty quantification using generalized interval analysis
Sensitivity analysis in kinetic Monte Carlo simulation based on random set sampling
Quantifying the effects of noise on early states of spinodal decomposition: CahneHilliardeCook equation and energy-based metrics
Uncertainty quantification of mesoscale models of porous uranium dioxide
Multiscale simulation of fiber composites with spatially varying uncertainties
Modeling non-Gaussian random fields of material properties in multiscale mechanics of materials
Fractal dimension indicator for damage detection in uncertain composites
Hierarchical multiscale model calibration and validation for materials applications
Efficient uncertainty propagation across continuum length scales for reliability estimates
Bayesian Global Optimization applied to the design of shape-memory alloys
An experimental approach for enhancing the predictability of mechanical properties of additively manufactured architected materials with manufacturing-induced variability
