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1. Uncertainty Quantification in Materials Modeling
2. The uncertainty pyramid for electronic-structure methods
3. Bayesian Error Estimation in Density Functional Theory
4. Uncertainty quantification of solute transport coefficients
5. Accelerating first-principles saddle point and local minimum search based on scalable Gaussian processes
6. Bayesian calibration of force fields for Molecular Simulations
7. Reliable molecular dynamics simulations for intrusive uncertainty quantification using generalized interval analysis
8. Sensitivity Analysis in Kinetic Monte Carlo Simulation based on Random Set Sampling
9. Quantifying the effects of noise on early states of spinodal decomposition: Cahn-Hilliard-Cook equation and energy-based metrics
10. Unce/rtainty Quantification of Mesoscale Models of Porous Uranium Dioxide
11. Multiscale Simulation of Fiber Composites in the Presence of Spatially-Varying Uncertainties
12. Modeling non-Gaussian random fields of material properties in multiscale mechanics of materials
13. Fractal dimension indicator for damage detection in uncertain composites
14. Hierarchical multiscale model calibration and validation for materials applications
15. Efficient uncertainty propagation across continuum length scales for reliability estimates
16. Bayesian Global Optimization applied to the Design of Shape Memory Alloys
17. An experimental approach for enhancing the predictability of mechanical properties of additively manufactured architected materials with manufacturing-induced variability
Uncertainty Quantification in Multiscale Materials Modeling provides a complete overview of uncertainty quantification in computational material science. It presents practical tools and methods alongside examples of their application to problems in materials modeling, thus allowing for UQ to be applied to various multiscale models. From the nanoscale, to the macroscale, the book presents a thorough synthesis of the state-of-the-art in uncertainty quantification, including Bayesian model inferences and insights for relevant material length scales.
- Synthesizes the research in uncertainty quantification (UQ) for materials modeling
- Provides practical tools and methods for problem-solving in materials modeling
- Enables unique configuration of UQ approaches in computational workflows
- Outlines verification of density functional theory methods and implementations
- Covers quantum-, atomistic-, mesoscale-, and multiscale-level modeling
Research scientists and engineers; graduate students; professors teaching UQMM
- No. of pages:
- © Woodhead Publishing 2020
- 1st March 2020
- Woodhead Publishing
- Paperback ISBN:
Dr. Wang’s research is in the areas of engineering design, manufacturing, modelling, and simulation. He currently studies geometric modelling approaches to support interactive computer-aided nano-design and simulation approaches to design and manufacture functional materials. He also works on methodologies to model and analyse uncertainties that are inherent in design and manufacturing. These include tolerance modelling, imprecise probability theory and its applications in system analysis and simulation, and numerical analysis for reliable computing. His overall research goal is to continuously develop and integrate knowledge of modelling, simulation, and visualization for product design and manufacturing. His educational objective is to help undergraduate and graduate students gain broader knowledge as well as analytical, computational, self-learning, or other skills as the foundation for their future careers.
Associate Professor, CAE and Design, Georgia Tech
Dr. McDowell joined Georgia Tech in 1983 and holds a dual appointment in the GWW School of Mechanical Engineering and the School of Materials Science and Engineering. He served as the Director of the Mechanical Properties Research Laboratory from 1992-2012. In 2012 he was named Founding Director of the Institute for Materials (IMat), one of Georgia Tech’s interdisciplinary Research Institutes charged with fostering an innovative ecosystem for research and education. He has served as Executive Director of IMat since 2013. His research focuses on nonlinear constitutive momcgodels for engineering materials, including cellular metallic materials, nonlinear and time dependent fracture mechanics, finite strain inelasticity and defect field mechanics, distributed damage evolution, constitutive relations and microstructure-sensitive computational approaches to deformation and damage of heterogeneous alloys, atomistic simulations of dislocation nucleation and mediation at grain boundaries, multiscale computational mechanics of materials ranging from atomistics to continuum, and systems-based computational materials design.
Executive Director, Georgia Institute for Materials, USA