Cogent Engineering

Engineering Applications of Trustworthy AI, Large Language Models, and Autonomous Agents

The rapid advancement of Artificial Intelligence (AI) is transforming engineering design, operation, maintenance, and decision-making across diverse industrial sectors. However, the successful deployment of AI in engineering environments requires more than predictive accuracy; it demands trustworthy, explainable, robust, and optimization-driven methodologies that can operate reliably under real-world constraints. This Article Collection aims to bring together cutting-edge research that advances the development and application of AI technologies for engineering systems while emphasizing methodological rigor, practical validation, and reproducibility.

This Collection seeks contributions that integrate AI, machine learning, optimization, and digital engineering frameworks to address complex challenges in manufacturing, energy, transportation, infrastructure, healthcare engineering, and industrial automation. This Collection aims to explore recent advances in Artificial Intelligence, Generative AI, Large Language Models (LLMs), Agentic AI, Machine Learning, and Optimization Techniques for engineering systems. Particular emphasis is placed on intelligent automation, autonomous decision-making, engineering design, digital twins, smart manufacturing, energy systems, and sustainable infrastructure. Contributions addressing the development of trustworthy, explainable, and optimization-driven AI solutions, as well as the integration of autonomous AI agents into engineering workflows, are especially encouraged. This Collection seeks both theoretical developments and practical applications that demonstrate the transformative impact of next-generation AI technologies on modern engineering practice. Particular emphasis is placed on deployable AI solutions, AI-enabled digital twins, intelligent decision-support systems, and optimization-driven engineering applications that demonstrate measurable performance improvements and real-world impact.

Submissions should provide strong engineering validation, comprehensive benchmarking against existing methods, and clear evidence of reproducibility. For AI-based approaches, authors are encouraged to address explainability, robustness, uncertainty quantification, fairness, safety, and reliability to ensure trustworthy deployment in engineering practice. Both theoretical developments and application-oriented studies are welcome, provided they contribute significant methodological innovation and engineering relevance.

The rapid adoption of Artificial Intelligence across engineering domains has brought increasing attention to regulatory compliance, governance frameworks, transparency, accountability, and risk management. To enhance the practical relevance and industrial impact of this Article Collection, we propose explicitly incorporating topics related to AI governance, compliance, and audit-readiness.

The expanded scope will encourage contributions addressing the development, deployment, and management of AI systems in accordance with emerging regulatory frameworks and industry standards. This addition will help bridge the gap between academic research and industrial implementation, while attracting submissions from practitioners, policymakers, and industry leaders concerned with responsible and trustworthy AI adoption.

The Potential topics that we suggest can be included are:

• AI Governance Frameworks for Engineering Systems
• Regulatory-Compliant AI and Engineering Applications
• AI Auditability, Traceability, and Documentation Practices
• Risk Assessment and Management in AI-Driven Engineering
• Explainable AI (XAI) for Regulatory and Industrial Requirements
• AI Lifecycle Governance and Model Monitoring
• Data Governance and Privacy-Preserving Engineering AI
• Regulatory Considerations for Generative AI and Large Language Models
• Compliance and Safety Assurance in Autonomous and Agentic AI Systems
• Governance of Digital Twins, Cyber-Physical Systems, and Industrial AI

This addition strengthens this Article Collection by aligning it with current global regulatory developments and the growing demand for trustworthy, compliant, and industry-ready AI solutions in engineering practice.

Keywords: Trustworthy AI, Engineering Optimization, Explainable AI (XAI), Large Language Models (LLMs), Agentic AI

Dr. Manoj Sahni is a dedicated and experienced Mathematics teacher and researcher with more than 21 years of experience and currently serving as a Professor in the Department of Mathematics, School of Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat, India. He has published more than 120 research papers in peer-reviewed Journals (SCI/ SCIE/ Scopus) and Conference Proceedings (Scopus Indexed) with reputed publishers like Springer, Elsevier, Taylor and Francis, and many more. In addition, he is a member of many international professional societies, including the AMS, SIAM, IEEE, MAA, FIM, and many more. He has written ten edited books, which were published by publishers like Springer, Taylor and Francis, NOVA, and River, and four are accepted by reputed publishers, and has also served as Guest Editor and Editorial Board Member for Special Issues published by reputed international publishers and scholarly journals.

Prof. José M. Merigó Lindahl is a Professor at the School of Information, Systems & Modelling at the Faculty of Engineering and Information Technology at the University of Technology Sydney (Australia) He has published more than 500 articles in journals, books and conference proceedings, including journals such as Information Sciences, IEEE Computational Intelligence Magazine, IEEE Transactions on Fuzzy Systems, European Journal of Operational Research, Expert Systems with Applications, International Journal of Intelligent Systems, Applied Soft Computing, Computers & Industrial Engineering and Knowledge-Based Systems. He has also published several books with Springer and with World Scientific. He is on the editorial board of several journals including Computers & Industrial Engineering, Applied Soft Computing, Technological and Economic Development of Economy, Journal of Intelligent & Fuzzy Systems, International Journal of Fuzzy Systems, Kybernetes, and Economic Computation and Economic Cybernetics Studies and Research. He has also been a guest editor for several international journals, a member of the scientific committee of several conferences, and a reviewer in a wide range of international journals. Recently, Thomson & Reuters (Clarivate Analytics) has distinguished him as a Highly Cited Researcher in Computer Science (2015-present). He is currently interested in Decision Making, Aggregation Operators, Computational Intelligence, Bibliometrics and Applications in Business and Economics.

Prof. Mohan Lal Kolhe is a full professor of Smart and Sustainable Electrical Energy Systems with Renewables and Hydrogen at the University of Agder’s Faculty of Engineering and Science in Norway. A globally respected renewable energy technologist, he brings over three decades of international academic leadership, having held esteemed positions at world-renowned institutions such as University College London (UK/Australia), University of Dundee (UK), University of Jyvaskyla (Finland), the Hydrogen Research Institute (Canada), and others. Consistently ranked among the top 2% of scientists worldwide by Stanford University (based on Elsevier data) since 2020, Prof. Kolhe continues to be a driving force in global efforts toward clean energy transitions — an influential researcher, policy advisor, and academic visionary.

The Guest Advisors declare no conflict of interest regarding this work.