Machine Learning and AI for Advanced Experimental Mechanics and Materials Design

Erscheinungsjahr 2027
ISBN: 978-0-443-40336-1
Verlag: Elsevier Science

Buch, Englisch, 400 Seiten, Format (B × H): 152 mm x 229 mm, Gewicht: 449 g

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.

Autoren/Hrsg.

Behera, Alok

Das, Debashish

Priya, Pikee

Fachgebiete

• Technische Wissenschaften Technik Allgemein Technik: Allgemeines

Inhaltsverzeichnis

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

Über Autor(innen)

Das, Debashish
Dr. Debashish Das is currently an Assistant Professor in the Mechanical Engineering department at the Indian Institute of Science in Bengaluru. Before joining IISc, he worked as a postdoctoral researcher at the University of Illinois at Urbana-Champaign (UIUC). He receivedt his Ph.D. in Aerospace Engineering also from UIUC in 2017. His research concentrates on finding new ways to understand how materials work. He uses different tools to study materials at different sizes and time scales. He also designs macroscale systems by taking advantage of micro and nanoscale properties

Behera, Alok
Dr Alok Behera is the Facility Manager-Research at the Department of Mechanical Engineering, the Indian Institute of Science (IISc), Bengaluru, India. Prior to his tenure at IISc, he held a position as a postdoctoral researcher in the Mechanical and Aerospace Engineering Department at the Indian Institute of Technology (IIT), Hyderabad. Dr. Behera obtained his Ph.D. from the Department of Metallurgical and Materials Engineering at Visvesvaraya National Institute of Technology (VNIT), Nagpur, India. His research expertise lies in advanced composites, sustainable manufacturing, and mechanics of polymer matrix composites

Priya, Pikee
Dr. Pikee Priya is an Assistant Professor in the department of Materials Engineering at the Indian Institute of Science, Bangalore. She has worked extensively on data-driven computational analysis of electrokinetics for energy applications, and microstructural design for traditional/additive manufacturing. Dr Priya received her doctoral degree in Materials Engineering from Purdue University in December 2016. She has a Masters degree from the Indian Institute of Science