Machine Learning and AI for Advanced Experimental Mechanics and Materials Design

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Machine Learning and AI for Advanced Experimental Mechanics and Materials Design

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  • 製本 Paperback:紙装版/ペーパーバック版/ページ数 400 p.
  • 言語 ENG
  • 商品コード 9780443403361

Full Description

Machine Learning and Artificial Intelligence in Experimental Mechanics and Materials Design is an up to date, comprehensive resource from which readers can gain a deep understanding of machine learning and artificial intelligence: tailored to experimental mechanics and materials design, ensuring a thorough grasp of these advanced technologies in context. Such a focus is not found elsewhere. The book demonstrates how to apply ML and AI in experimental settings through real-world examples of case studies, accelerating materials discovery and design processes effectively. The ethical complexities associated with ML and AI in experimental research are explored, equipping readers with the knowledge to address biases and ethical dilemmas responsibly. Using a problem-solving approach, the book describes how to overcome daily challenges encountered in experimental mechanics and materials design with practical solutions and methodologies, empowering readers to achieve their research goals efficiently. The book provides insights into adopting best practice for implementation of research outcomes. It sets out the current trends and future opportunities for this rapidly developing field.

Contents

1. Fundamentals of Machine Learning and Artificial Intelligence
2. Fundamentals of Experimental Mechanics
3. Introduction to the Role of ML in Experimental Mechanics
4. Data-Driven Approaches for High Throughput Experiments and Processing-Property Analyses
5. Experimental and Modeling Challenges in a Machine-Learning Environment in Mechanics
6. A Machine Learning Framework for Accelerated Materials Discovery and Design using Artificial Intelligence and Machine Learning
7. A Data Resource for Emerging Materials and the Challenges for Data Science and Design
8. Artificial Intelligence and Machine Learning Driven Structural Health Monitoring and Damage Detection in Experimental Mechanics and Materials
9. Physics-Informed Neural Networks for Experimental Mechanics
10. Ethical Considerations and Bias in Machine Learning Applications

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