Machine Learning and AI for Advanced Experimental Mechanics and Materials Design

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 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 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

• 1. Fundamentals of Machine Learning and Artificial Intelligence2. Fundamentals of Experimental Mechanics3. Introduction to the Role of ML in Experimental Mechanics4. Data-Driven Approaches for High Throughput Experiments and Processing-Property Analyses5. Experimental and Modeling Challenges in a Machine-Learning Environment in Mechanics6. A Machine Learning Framework for Accelerated Materials Discovery and Design using Artificial Intelligence and Machine Learning7. A Data Resource for Emerging Materials and the Challenges for Data Science and Design8. Artificial Intelligence and Machine Learning Driven Structural Health Monitoring and Damage Detection in Experimental Mechanics and Materials9. Physics-Informed Neural Networks for Experimental Mechanics10. Ethical Considerations and Bias in Machine Learning Applications