Uncertainty Quantification of Stochastic Defects in Materials
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Uncertainty Quantification of Stochastic Defects in Materials
Chu, Liu
Taylor & Francis Ltd
05/2024
196
Mole
9781032128757
Pré-lançamento - envio 15 a 20 dias após a sua edição
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1. Overview. 2. Stochastic Defects. Part I: Methods and Theories. 3. Monte Carlo Methods. 4. Polynomial Chaos Expansion. 5. Stochastic Finite Element Method. 6. Machine Learning Methods. Part II: Examples. 7. Numerical Examples. 8. Monte Carlo-based Finite Element Method. 9. Impacts of Vacancy Defects in Resonant Vibration. 10. Uncertainty Quantification in Nanomaterial. 11. Equivalent Young's Modulus Prediction. 12. Strengthen Possibility by Random Vacancy Defects.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Stochastic Defects;materials characterization;Graphene Sheets;computational materials science;Kriging Surrogate Model;finite element method;Probability Density Distribution;materials defects;Zigzag Edges;microstructure defect;Equivalent Young's Modulus;material properties;Stochastic Finite Element Method;Pristine Graphene;Armchair Edge;Energy Density;Natural Frequencies;Finite Element Model;Kl Expansion;Modified Couple Stress Theory;Kriging Model;Modified Morse Potential;Restricted Boltzmann Machine;MC;Importance Sampling;ANSYS Parameter Design Language;Uniaxial Tension;Subset Simulation;Total Strain;Prediction Results;Cumulative Distribution Function
1. Overview. 2. Stochastic Defects. Part I: Methods and Theories. 3. Monte Carlo Methods. 4. Polynomial Chaos Expansion. 5. Stochastic Finite Element Method. 6. Machine Learning Methods. Part II: Examples. 7. Numerical Examples. 8. Monte Carlo-based Finite Element Method. 9. Impacts of Vacancy Defects in Resonant Vibration. 10. Uncertainty Quantification in Nanomaterial. 11. Equivalent Young's Modulus Prediction. 12. Strengthen Possibility by Random Vacancy Defects.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Stochastic Defects;materials characterization;Graphene Sheets;computational materials science;Kriging Surrogate Model;finite element method;Probability Density Distribution;materials defects;Zigzag Edges;microstructure defect;Equivalent Young's Modulus;material properties;Stochastic Finite Element Method;Pristine Graphene;Armchair Edge;Energy Density;Natural Frequencies;Finite Element Model;Kl Expansion;Modified Couple Stress Theory;Kriging Model;Modified Morse Potential;Restricted Boltzmann Machine;MC;Importance Sampling;ANSYS Parameter Design Language;Uniaxial Tension;Subset Simulation;Total Strain;Prediction Results;Cumulative Distribution Function
