| ISBN13: | 9781032508856 |
| ISBN10: | 103250885X |
| Binding: | Hardback |
| No. of pages: | 220 pages |
| Size: | 229x152 mm |
| Language: | English |
| Illustrations: | 86 Illustrations, black & white; 44 Halftones, black & white; 42 Line drawings, black & white; 15 Tables, black & white |
| 700 |
Probability and mathematical statistics
Optimization, linear programming, game theory
Electrical engineering and telecommunications, precision engineering
Mechanical Engineering Sciences
Civil and construction engineering
Computer architecture, logic design
Artificial Intelligence
Product design
Probability and mathematical statistics (charity campaign)
Optimization, linear programming, game theory (charity campaign)
Electrical engineering and telecommunications, precision engineering (charity campaign)
Mechanical Engineering Sciences (charity campaign)
Civil and construction engineering (charity campaign)
Computer architecture, logic design (charity campaign)
Artificial Intelligence (charity campaign)
Product design (charity campaign)
Artificial Intelligence Assisted Structural Optimization
GBP 110.00
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Artificial Intelligence Assisted Structural Optimization explores the use of machine learning and correlation analysis within the forward design and inverse design frameworks to design and optimize lightweight load bearing structures as well as mechanical metamaterials.
Artificial Intelligence Assisted Structural Optimization explores the use of machine learning and correlation analysis within the forward design and inverse design frameworks to design and optimize lightweight load-bearing structures as well as mechanical metamaterials.
Discussing both machine learning and design analysis in detail, this book enables readers to optimize their designs using a data-driven approach. This book discusses the basics of the materials utilized, for example, shape memory polymers, and the manufacturing approach employed, such as 3D or 4D printing. Additionally, the book discusses the use of forward design and inverse design frameworks to discover novel lattice unit cells and thin-walled cellular unit cells with enhanced mechanical and functional properties such as increased mechanical strength, heightened natural frequency, strengthened impact tolerance, and improved recovery stress. Inverse design methodologies using generative adversarial networks are proposed to further investigate and improve these structures. Detailed discussions on fingerprinting approaches, machine learning models, structure screening techniques, and typical Python codes are provided in the book.
The book provides detailed guidance for both students and industry engineers to optimize their structural designs using machine learning.
1. Introduction to Structures with Complex Geometrical Configurations. 2. Structural Optimization. 3. Introduction to Machine Learning-Assisted Structural Optimization. 4. Structural Optimization of Biomimetic Rods Using Machine Learning Regression. 5. Structural Optimization of Lattice Structures. 6. Inverse Machine Learning Using Generative Adversarial Networks. 7. Design and Optimization of Mechanical Metamaterials Using Correlation Analysis. 8. Summary and Future Perspectives.
