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Uncertainty Quantification in Multiscale Materials Modeling. Elsevier Series in Mechanics of Advanced Materials

  • ID: 4850191
  • Book
  • March 2020
  • 604 Pages
  • Elsevier Science and Technology
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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.

  • Synthesizes available UQ methods for materials modeling
  • Provides practical tools and examples for problem solving in modeling material behavior across various length scales
  • Demonstrates UQ in density functional theory, molecular dynamics, kinetic Monte Carlo, phase field, finite element method, multiscale modeling, and to support decision making in materials design
  • Covers quantum, atomistic, mesoscale, and engineering structure-level modeling and simulation

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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. Data-driven acceleration of 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: CahneHilliardeCook equation and energy-based metrics
  10. Uncertainty quantification of mesoscale models of porous uranium dioxide
  11. Multiscale simulation of fiber composites with 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
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Wang, Yan
Dr. Wang joined Georgia Tech in 2009 and leads the Multiscale Systems Engineering research group. His research areas include computer-aided design, computer-aided manufacturing, modeling and simulation, as well as uncertainty quantification. The overarching goal of his research group is to tackle the curse-of-dimensionality design challenge by developing new physics-based data-driven methods to enable engineers to establish comprehensive and robust process-structure-property relationships for the design of materials, products, and processes. He has published over 90 archived journal papers and 80 peer-reviewed conference papers. His research work was recognized with multiple best paper awards at American Society of Mechanical Engineers (ASME), Institute of Industrial and Systems Engineers (IISE), Minerals, Metals and Materials Society (TMS), and Computer-Aided Design (CAD) conferences, as well as the U.S. National Science Foundation CAREER Award. He has been regularly invited to give lectures at universities in U.S., Europe, and Asia, and review proposals for government agencies of several countries. He served as editors for ASME Journal of Computing & Information Science in Engineering, Journal of Mechanical Design, Journal of Computational & Nonlinear Dynamics, and Journal of Risk & Uncertainty in Engineering Systems. He is currently the Chair of the ASME Computers & Information in Engineering Division, and was the Chair of ASME Advanced Modeling & Simulation Technical Committee.
McDowell, David L.
Dr. McDowell joined Georgia Tech in 1983 and holds a dual appointment in the Woodruff 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 the development of physically-based, microstructure-sensitive constitutive models for nonlinear and time-dependent behavior of materials, with emphasis on wrought and cast metals. Topics of interest include finite strain inelasticity and defect field mechanics, microstructure-sensitive computational approaches to deformation and damage of heterogeneous materials, with emphasis on metal fatigue, atomistic and coarse-grained atomistic simulations of dislocations, dynamic deformation and failure of materials, irradiation effects on materials, and multiscale modeling with methods for uncertainty quantification. He has contributed to schemes for computational materials science and mechanics to inform systems design of materials. Applications of current interest span lightweight structural materials, materials for hot sections of aircraft gas turbine engines, titanium alloys, ferritic and austenitic alloys, and nanocrystalline materials, among others.
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