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
INVITED TALKS
Selected Talks
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 THRUSTS
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
MOMENTS
Lab Activities
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 delivers an invited talk titled "Physical AI for Manufacturing: A Call for Clarity and Rigor" at the International Conference on Precision Engineering and Sustainable Manufacturing (PRESM 2026).
Video Link
Abstract: Artificial intelligence (AI) is rapidly evolving beyond purely digital domains into the physical world, giving rise to what is now referred to as Physical AI. While the concept is gaining increasing attention, its definition and scope remain not yet fully established. Physical AI encompasses intelligent systems capable of perceiving, reasoning, and acting within real-world environments through integration with machines and engineering systems. Moving beyond conventional automation, these systems are advancing toward embodied intelligence, where they can learn from interaction, adapt to dynamic conditions, and continuously improve their performance in complex and unstructured environments. This talk provides a systematic overview of the current state of Physical AI, aiming to bring greater clarity and rigor to the field. The seminar also addresses critical challenges facing Physical AI in manufacturing, along with strategies to overcome these limitations. Ultimately, this talk aims to provide a clearer and more rigorous perspective on how Physical AI can be effectively defined and deployed to enable future manufacturing systems.
- Hyeongbae: Diffusion Schrödinger Bridge Framework for Path-aware Stochastic Transport in Manufacturing
- Jeongwoo: A Differentiable Physics-based Framework for Hybrid Discrete-Continuous Process Optimization