Machine Learning and AI for Advanced Experimental Mechanics and Materials Design
Will be published approx. on 28. February 2027
Book
Paperback/Softback
400 pages
978-0-443-40336-1 (ISBN)
Description
Machine Learning and Artificial Intelligence in Experimental Mechanics and Materials Design is a comprehensive resource on machine learning and artificial intelligence tailored to experimental mechanics and materials design. The book demonstrates how to apply ML and AI in experimental settings through real-world case studies, effectively accelerating materials discovery and design processes. 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.
Finally, the book provides insights into adopting best practice for the implementation of research outcomes by setting out current trends and future opportunities in this rapidly developing field.
Finally, the book provides insights into adopting best practice for the implementation of research outcomes by setting out current trends and future opportunities in this rapidly developing field.
More details
Content
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
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