Deadline Date: 28 February 2027
The rapid evolution of large language models (LLMs) and generative AI has significantly advanced artificial intelligence, yet integrating these models into computational engineering and numerical design introduces critical challenges regarding security, integrity, and structural trustworthiness. For AI to be safely deployed in engineering environments—such as infrastructure design, fluid dynamics, and material science—addressing these issues is essential.
This Special Issue aims to explore the synergy between LLMs and computational engineering. It focuses on methodologies that enhance the robustness and reliability of AI-driven numerical methods and engineering simulations. We welcome contributions that discuss how LLMs can assist in automated engineering design, code generation for numerical solvers, and the interpretation of complex computational data. The issue seeks both theoretical and practical implementations that bridge the gap between advanced AI and dependable engineering calculations.
Topics of interest include, but are not limited to:
AI-augmented numerical methods and structural robustness
Trustworthy LLMs for automated engineering design and simulation
Security and integrity of AI-generated computational models
Explainable AI (XAI) for complex engineering decision-making
Pattern recognition and computer vision in structural health monitoring
Human–computer interaction for CAD and intelligent engineering software
Physics-informed machine learning for secure engineering applications
Privacy-preserving AI frameworks in collaborative engineering projects
Editorial board
