Uncertainty Quantification in Multiscale Materials Modeling
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...
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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.
Bibliographische Angaben
- Herausgegeben:Wang, Yan; McDowell, David L.
- Verlag: Woodhead Publishing
- EAN: 9780081029411
Autoren-Porträt
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.
Inhaltsverzeichnis zu „Uncertainty Quantification in Multiscale Materials Modeling “
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
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