Smart Materials Engineering: Data-Driven Approaches and Multiscale Modelling - Tapa dura

Acerca del autor

Dr. Ali Ahmadian, a globally recognized researcher in applied mathematics, specializes in fuzzy and fractional calculus and has authored over 400 papers and several books.

Dr. Sambhrant Srivastava, an expert in mechanical engineering and biomaterials, is known for his work on bio-composite materials for dental implants.

Dr. Ashok Kumar Yadav brings expertise in MADM, blockchain, machine learning, and mobile communication, with numerous impactful publications and patents.

Mr. Vijay Kumar, with interests in vibration, ML, and composite materials, has contributed to prestigious journals and holds patents and books.

Dr. Pramod Kumar Srivastava, an experienced academic and Dean at REC Azamgarh, excels in wireless sensor networks and mathematical modeling, with a strong record of Ph.D. mentorship and gold-medal academic excellence. Together, they offer a strong multidisciplinary foundation, blending advanced research, innovation, and academic leadership across applied mathematics, engineering, and computational intelligence.

De la contraportada

This book bridges the gap between conventional materials science and emerging data-driven methodologies, highlighting the integration of AI, machine learning, and deep learning technologies to enhance the design, analysis, and optimization of smart materials. It provides a holistic perspective essential for researchers, engineers, and students exploring the intersection of materials engineering and AI technologies.

The book examines the connection between recent advancements in materials science and multiscale machine learning, facilitating predictive and prescriptive modeling for assessing material behavior based on composition, structure, and processing. It includes comprehensive discussions on smart material design, optimization, complexity analysis, and advanced computational methods for synthesizing and characterizing materials. Challenges in multiscale modeling, such as biologically inspired material design and the influence of nanotechnology on current trends, are thoroughly explored.

Emphasizing the critical role of multiscale machine learning and nanotechnology in creating sustainable smart materials, the book also addresses the ethical implications of this research. It discusses opportunities and challenges in biomaterials, particularly in healthcare and biomedical applications, and anticipates future trends in machine learning for sustainable materials design. The book provides insights into how predictive and prescriptive modeling through machine learning can accelerate the material discovery process, guiding researchers toward promising candidates for further exploration.

Serving as a roadmap for researchers and scientists, this book offers valuable insights into innovative approaches that support the future of materials science.