Guest Editors
Assoc. Prof. Rupendra Kumar Pachauri
Email: rupendrapachauri@gmail.com
Affiliation: Electrical Cluster, School of Advanced Engineering, UPES, India
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Research Interests: Machine Learning | Deep Learning | Artificial Intelligence | Image Processing
Summary
The rapid evolution of Artificial Intelligence (AI) has profoundly transformed the landscape of engineering and scientific research. Once considered a futuristic concept, AI is now an essential driver of innovation across disciplinesranging from mechanical and civil engineering to physics, biology, and environmental sciences.
In engineering, AI techniques such as machine learning, neural networks, and evolutionary algorithms are revolutionizing areas like structural health monitoring, materials design, predictive maintenance, and process optimization. Meanwhile, in the sciences, AI is accelerating data analysis in genomics, climate modeling, and chemical simulations, enabling insights at scales previously unimaginable.
This special issue aims to capture the latest breakthroughs and applications of AI in engineering and scientific domains. By showcasing innovative methods and practical implementations, it seeks to bridge the gap between theoretical development and real-world impact, fostering a deeper understanding of how AI can shape the future of research and industry.
Aim and Scope
This Special Issue aims to highlight recent advances in Artificial Intelligence (AI) applications across engineering and scientific disciplines. It invites original research, reviews, and case studies that demonstrate AI's role in solving complex problems, enhancing system performance, and driving innovation. Topics include machine learning, deep learning, AI-driven optimization, intelligent sensing, predictive analytics, and interdisciplinary AI applications in areas such as energy systems, materials science, environmental monitoring, and biomedical engineering. The issue seeks to bridge theory and practice, fostering collaboration among researchers, practitioners, and technologists to accelerate the integration of AI into real-world engineering and scientific challenges.
Suggested themes
·AI in Sustainable and Smart Engineering Systems
·Machine Learning and Deep Learning for Scientific Data Analysis
·Intelligent Control and Optimization in Industrial Applications
·AI-Driven Design and Simulation in Materials and Structural Engineering
·Applications of AI in Environmental and Energy Systems
·Interdisciplinary Approaches: AI in Biomedical, Agricultural, and Climate Sciences
Keywords
Artificial Intelligence, Machine Learning, Deep Learning, Engineering Applications, Scientific Computing, Intelligent Systems, Predictive Analytics, Optimization, Smart Technologies, Interdisciplinary Research
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