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· 제목 : Computational Mechanics with Neural Networks (Hardcover)
· 분류 : 외국도서 > 기술공학 > 기술공학 > 기계공학
· ISBN : 9783030661106
· 쪽수 : 228쪽
· 출판일 : 2021-02-27
· 분류 : 외국도서 > 기술공학 > 기술공학 > 기계공학
· ISBN : 9783030661106
· 쪽수 : 228쪽
· 출판일 : 2021-02-27
목차
Part I Preliminaries: Machine Learning Technologies for Computational Mechanics
1. Computers and Network
1.1 Computers and Processors
1.2 Network Technologies
1.3 Parallel Processing
1.4 Numerical Precision
2. Feedforward Neural Networks
2.1 Bases
2.2 Various Types of Layers
2.3 Regularization
2.4 Acceleration for Training
2.5 Initialization of Connection Weights
2.6 Model Averaging and Dropout
3. Deep Learning
3.1 Neural Network vs. Deep Learning
3.2 Pretraining: Autoencoder
3.3 Pretraining: Restricted Boltzmann Machine
4. Mutually Connected Neural Networks
4.1 Hopfield Network
4.2 Boltzmann Machine
5. Other Neural Networks
5.1 Self-Organizing Maps5.2 Radial Basis Function Networks
6. Other Algorithms and Systems
6.1 Genetic Algorithms
6.2 Genetic Programming
6.3 Other Bio-inspired Algorithms
6.4 Support Vector Machines
6.5 Expert Systems
6.6 Software Tools
Part II Applications
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7. Introductory Remarks
8. Constitutive Models
8.1 Parameter Determination of Viscoplastic Constitutive Equations
8.2 Implicit Constitutive Modelling for Viscoplasticity
8.3 Autoprogressive Algorithm
8.4 Others
9. Numerical Quadrature
9.1 Optimization of Number of Quadrature Points
9.2 Optimization of Quadrature Parameters
10. Identifications of Analysis Parameters
10.1 Time Step Evaluation of Pseudo Time-dependent Stress Analysis
10.2 Parameter Identification of Augmented Lagrangian Method
10.3 Predictor-Corrector Method for Structural Nonlinear Analysis
10.4 Contact Stiffness Estimation
11. Solvers and Solution Methods
11.1 Finite Element Solutions through Direct Minimization of Energy Functionals
11.2 Neurocomputing Model for Elastoplasticity
11.3 Structural Re-analysis
11.4 Simulations of Global Flexibility and Element Stiffness
11.5 Solutions based on Variational Principle
11.6 Boundary Conditions
11.7 Hybrid Graph-Neural Method for Domain Decomposition
11.8 Wavefront Reduction
11.9 Contact Search
11.10 Physics-informed Neural Networks
11.11 Dynamic Analysis with Explicit Time Integration Scheme
11.12 Reduced Order Model for Improvement of Solutions using Coarse Mesh
12. Structural Identification
12.1 Identification of Defects with Laser Ultrasonics
12.2 Identification of Cracks
12.3 Estimation of Stable Crack Growth
12.4 Failure Mechanisms in Power Plant Components
12.5 Identification of Parameters of Non-uniform Beam
12.6 Prediction of Beam-Mass Vibration
12.7 Others
12.7.1 Nondestructive Evaluation with Neural Networks
12.7.2 Structural Identification with Neural Networks
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12.7.3 Neural Networks Combined with Global Optimization Method
12.7.4 Training of Neural Networks
13. Structural Optimization
13.1 Hole Image Interpretation for Integrated Topology and Shape Optimization
13.2 Preform Tool Shape Optimization and Redesign
13.3 Evolutionary Methods for Structural Optimization with Adaptive Neural Networks
13.4 Optimal Design of Materials
13.5 Optimization of Production Process
13.6 Estimation and Control of Dynamic Behaviors of Structures
13.7 Subjective Evaluations for Handling and Stability of Vehicle
13.8 Others
14. Some Notes on Applications of Neural Networks to Computational Mechanics
14.1 Comparison among Neural Networks, and Other AI Technologies
14.2 Improvements of Neural Networks in terms of Applications to
Computational Mechanics
15. Other AI Technologies for Computational Mechanics
15.1 Parameter Identification of Constitutive Model
15.2 Constitutive Material Model by Genetic Programming
15.3 Data-driven Analysis without Material Modelling15.4 Numerical Quadrature
15.5 Contact Search using Genetic Algorithm
15.6 Contact Search using Genetic Programming
15.7 Solving Non-linear Equation Systems using Genetic Algorithm
15.8 Nondestructive Evaluation
15.9 Structural Optimization
15.10 Others
16. Deep Learning for Computational Mechanics
16.1 Neural Networks versus Deep Learning
16.2 Applications of Deep Convolutional Neural Networks to Computational Mechanics
16.3 Applications of Deep Feedforward Neural Networks to Computational Mechanics
16.4 Others
Appendix
A1 Bases of Finite Element Method
A2 Parallel Processing for Finite Element Method
A3 Isogeometric Analysis
A4 Free Mesh Method
A5 Other Meshless Methods
A6 Inverse Problems
