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Uncertainty Quantification in Multiscale Materials Modeling

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Beschreibung

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...

Beschreibung

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.

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

27.03.2020

Herausgeber Yan Wang + weitere
Verlag Elsevier Science & Technology

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

27.03.2020

Herausgeber
Verlag Elsevier Science & Technology
Seitenzahl

606

Maße (L/B/H)

22,9/15,2/3,1 cm

Gewicht

801 g

Sprache

Englisch

ISBN

978-0-08-102941-1

EU-Ansprechpartner

Elsevier B.V.
Radarweg 29
1043 NX Amsterdam
NL
productsafety@elsevier.com

Herstelleradresse

Elsevier Science & Technology
London Wall 125
EC2Y 5AS London
GB
tradeorders@elsevier.com

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  • Produktbild: Uncertainty Quantification in Multiscale Materials Modeling
  • Produktbild: Uncertainty Quantification in Multiscale Materials Modeling
  • 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