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 materials modeling. UQ methods are applied t...
Gespeichert in:
| Weitere Verfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Duxford, United Kingdom
Woodhead Publishing, Elsevier
[2020]
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| Schriftenreihe: | Elsevier series in mechanics of advanced materials
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| Schlagworte: | |
| Zusammenfassung: | 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 |
| Beschreibung: | xviii, 586 Seiten Illustrationen, Diagramme 229 mm |
| ISBN: | 9780081029411 |
Internformat
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| 245 | 1 | 0 | |a Uncertainty quantification in multiscale materials modeling |c edited by Yan Wang and David L. McDowell |
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| 520 | |a 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 | ||
| 655 | 7 | |0 (DE-588)4143413-4 |a Aufsatzsammlung |2 gnd-content | |
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Datensatz im Suchindex
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| spelling | Uncertainty quantification in multiscale materials modeling edited by Yan Wang and David L. McDowell Duxford, United Kingdom Woodhead Publishing, Elsevier [2020] xviii, 586 Seiten Illustrationen, Diagramme 229 mm txt rdacontent n rdamedia nc rdacarrier Elsevier series in mechanics of advanced materials 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 (DE-588)4143413-4 Aufsatzsammlung gnd-content Wang, Yan edt McDowell, David L. 1956- (DE-588)1067788972 edt |
| spellingShingle | Uncertainty quantification in multiscale materials modeling |
| subject_GND | (DE-588)4143413-4 |
| title | Uncertainty quantification in multiscale materials modeling |
| title_auth | Uncertainty quantification in multiscale materials modeling |
| title_exact_search | Uncertainty quantification in multiscale materials modeling |
| title_exact_search_txtP | Uncertainty quantification in multiscale materials modeling |
| title_full | Uncertainty quantification in multiscale materials modeling edited by Yan Wang and David L. McDowell |
| title_fullStr | Uncertainty quantification in multiscale materials modeling edited by Yan Wang and David L. McDowell |
| title_full_unstemmed | Uncertainty quantification in multiscale materials modeling edited by Yan Wang and David L. McDowell |
| title_short | Uncertainty quantification in multiscale materials modeling |
| title_sort | uncertainty quantification in multiscale materials modeling |
| topic_facet | Aufsatzsammlung |
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