Название: Artificial Intelligence Assisted Structural OptimizationАвтор: Adithya Challapalli, Guoqiang Li
Издательство: CRC Press
Год: 2025
Страниц: 221
Язык: английский
Формат: pdf (true), epub
Размер: 23.0 MB
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.
Within these pages, you will find a comprehensive exploration of the fundamentals of Machine Learning, as well as its application to the design of lightweight structures. From the basics of data analysis to the complexities of neural networks and Deep Learning, this book provides a step-by-step guide to the tools and techniques that are transforming the field. The remainder of this chapter will introduce different Machine Learning algorithms with basic Python code snippets to understand and compare the performance of each type. Basic coding and Python language knowledge are required.
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.
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