Debashish Das – författare

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.Provides comprehensive coverage on how Machine Learning (ML) and Artificial Intelligence (AI) are specifically tailored to experimental mechanics and materials designBridges the gap between sophisticated, data-driven methodologies and conventional experimental techniquesDelivers workable answers to the main problems encountered by researchers and professionals in the areaCovers advanced machine learning techniques applicable to structural health monitoring and data-driven designDemonstrates the application of ML and AI from experimental settings to real-world examplesEmploys a problem-solving approach for effectively dealing with the challenges encountered in experimental mechanics and materials design

This book explores the development of several new learning algorithms that utilize recent optimization techniques and meta-heuristics. It addresses well-known models such as particle swarm optimization, genetic algorithm, ant colony optimization, evolutionary strategy, population-based incremental learning, and grey wolf optimizer for training neural networks. Additionally, the book examines the challenges associated with these processes in detail. This volume will serve as a valuable reference for individuals in both academia and industry.