دانلود کتاب Manifold Learning: Model Reduction in Engineering (SpringerBriefs in Computer Science)

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Foreword
Contents
Acronyms
1 Structured Data and Knowledge in Model-Based Engineering
1.1 Nomenclature
1.2 Model-Based Engineering
1.3 Motivation
References
2 Learning Projection-Based Reduced-Order Models
2.1 Motivation and Basic Assumptions
2.2 High-Fidelity Model (HFM)
2.3 Linear Manifold Learning for Projection-Based …
2.3.1 Approaches Preceding the Use of Machine Learning
2.3.2 Online Phase: Galerkin Projection
2.3.3 Offline Phase
2.3.4 Hyper-Reduction via a Reduced Integration Domain
2.3.5 Hyper-Reduction via Empirical Cubature
2.3.6 Computational Complexity
2.4 Nonlinear Manifold Learning for Projection-Based Reduced-Order Modeling
2.4.1 Nonlinear Dimensionality Reduction via Auto-Encoder
2.4.2 Piecewise Linear Dimensionality Reduction via Dictionary-Based ROM-Nets
2.5 Iterative and Greedy Strategies
References
3 Error Estimation
3.1 Confidence and Trust in Model-Based Engineering Assisted by AI
3.2 In Linear Elasticity and for Linear Problems
3.3 In Nonlinear Mechanics of Materials
3.4 In Computational Fluid Dynamics
3.5 A Note on Accuracy of a Posteriori Error Bounds and Round-Off Errors
References
4 Resources: Software and Tutorials
4.1 Mordicus: Reduced-Order Methods Designed for Industrial Usage
4.1.1 Mordicus Project and Consortium
4.1.2 Mordicus Library
4.1.3 Mordicus Datamodel
4.2 GenericROM Library
4.2.1 Main Available Methods
4.2.2 Noninstrusivity and Nonparametrized Variability
4.2.3 Precomputations for Efficiency
4.2.4 Tutorials and Datasets
References
5 Industrial Application: Uncertainty Quantification in Lifetime Prediction of Turbine Blades
5.1 Industrial Context
5.1.1 Thermomechanical Fatigue of High-Pressure Turbine Blades
5.1.2 Industrial Dataset and Objectives
5.1.3 Modeling Assumptions
5.1.4 Stochastic Model for the Thermal Loading
5.2 ROM-net Based Uncertainty Quantification Applied to an Industrial …
5.2.1 Design of Numerical Experiments
5.2.2 ROM Dictionary Construction
5.2.3 Automatic Model Recommendation
5.2.4 Surrogate Model for Gappy Reconstruction
5.2.5 Uncertainty Quantification Results
5.2.6 Workflow
5.2.7 Verification
References
6 Applications and Extensions: A Survey of Literature
6.1 Linear Manifold Learning
6.2 Nonlinear Dimensionality Reduction via Auto-Encoder
6.3 Piecewise Linear Dimensionality Reduction via Dictionary-Based …
6.4 Extension: Manifold Learning of Physics Problems Assisted …
References