Industrial AI Lab
School of Mechanical Engineering,
Chung-Ang University
중앙대학교 산업 인공지능 연구실
Industrial AI Lab
School of Mechanical Engineering, Chung-Ang University
중앙대학교 산업 인공지능 연구실
NEWS
Recent News at IAI Lab
NEWS
Recent News
at IAI Lab
OUR VISION
Engineering Meets AI,
AI Meets Engineering.
The overarching objective of the IAI Lab is to pioneer AI-driven advancements by leveraging the strength of engineering background. By integrating the knowledge of diverse engineering disciplines ranging from mechanical engineering, physics, and computer science, we harness the full potential of artificial intelligence (AI) in redefining engineering processes and outcomes. We are dedicated to developing advanced AI methods based on engineering and data-driven insights, enabling us to model the complexities of various engineering systems well. Our strategies extend beyond conventional and existing approaches by empowering AI-enabled engineering, thereby facilitating unprecedented analysis, decision-making, optimization, and so on. Through this synergy of engineering and AI, the IAI Lab strives to pioneer a new wave of AI innovation, elevating the engineering landscape and further making the future industry smarter and more efficient.
RESEARCH THRUST
Towards Seamless Industrial AI
IAI Lab conducts extensive research to seamlessly integrate artificial intelligence (AI) across various industrial aspects, from physical phenomena to manufacturing processes, aiming to enhance efficiency, predictability, and intelligent functionality.
Generative/Agentic AI for Engineering
Collaborative and exploratory AI-driven decision-making for autonomous engineering process
AI for Future Manufacturing
AI-enabled intelligent and autonomous manufacturing enabling adaptive, efficient, and resilient production
Extended Physical AI (E-PAI)
Physical intelligence with mechanistic understanding and knowledge integration for generalizable real-world interaction
CORE APPLICATIONS
AI+X Impacts
OUR VISION
The overarching objective of the IAI Lab is to pioneer AI-driven advancements by leveraging the strength of engineering background. By integrating the knowledge of diverse engineering disciplines ranging from mechanical engineering, physics, and computer science, we harness the full potential of artificial intelligence (AI) in redefining engineering processes and outcomes. We are dedicated to developing advanced AI methods based on engineering and data-driven insights, enabling us to model the complexities of various engineering systems well. Our strategies extend beyond conventional and existing approaches by empowering AI-enabled engineering, thereby facilitating unprecedented analysis, decision-making, optimization, and so on. Through this synergy of engineering and AI, the IAI Lab strives to pioneer a new wave of AI innovation, elevating the engineering landscape and further making the future industry smarter and more efficient.
RESEARCH THRUSTS
IAI Lab conducts extensive research to seamlessly integrate artificial intelligence (AI) across various industrial aspects, from physical phenomena to manufacturing processes, aiming to enhance efficiency, predictability, and intelligent functionality.
CORE APPLICATIONS
AI+X Impacts
MOMENTS
Lab Activities
Prof. Sooyoung Lee gives an invited seminar at the Department of Mechanical Engineering, Seoul National University (SNU).
Title: "Engineered Artificial Intelligence for Future Design & Manufacturing"
Abstract: Recent advances in artificial intelligence (AI) have rapidly expanded the capabilities of data-driven methods across a variety of industries. However, effectively applying AI to engineering applications requires approaches that incorporate established knowledge, underlying principles and mechanisms, and domain-specific constraints. This seminar introduces Engineered AI as a framework for developing AI technologies specifically designed for future design and manufacturing systems. The central idea is that AI for engineering should move beyond simple adoption of existing models toward domain-specialized AI development that systematically integrates engineering knowledge. We present diverse research directions and technology development cases, including generative and agentic AI for autonomous engineering workflows, large language model (LLM)-driven engineering informatics, and mechanistic or physics-guided AI for engineering analysis and decision support. The seminar ultimately emphasizes the complementary roles of engineering-tailored AI and AI-assisted engineering, highlighting how their co-evolution can enable more reliable and intelligent innovation in next-generation engineering.