Industrial AI Laboratory (IAI Lab)

School of Mechanical Engineering,

Chung-Ang University

중앙대학교 산업 인공지능 연구실

Industrial AI Laboratory (IAI Lab)
School of Mechanical Engineering, Chung-Ang University

중앙대학교 산업 인공지능 연구실

OUR VISION

Engineering Meets AI,

AI Meets Engineering.

Generated by diffusion model
Generated by diffusion model

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 physics-guided 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-aided engineering, thereby facilitating unprecedented perception, 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.

Physics-guided AI

Scientific and engineering knowledge-guided AI for generalization

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Generative AI for Engineering

Novel discovery of engineering structures and materials for desired properties

View more

AI for Smart Manufacturing

Data-driven approaches to unravel the relations between process parameters and outcomes

View more

Advanced System Intelligence

Innovative metrology, perception and monitoring for diverse engineering systems

View more

CORE APPLICATIONS

AI+X Impacts

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 physics-guided 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-aided engineering, thereby facilitating unprecedented perception, 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.

(Generated by diffusion model)
(Generated by diffusion model)

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.

Physics-guided AI 

Scientific and engineering knowledge-guided AI for generalization

View more

AI for Smart Manufacturing

Data-driven approaches to unravel the relations between process parameters and outcomes

View more

Generative AI for Engineering

Novel discovery of engineering structures and materials for desired properties

View more

Advanced System Intelligence

Innovative metrology, perception and monitoring for diverse engineering systems

View more

CORE APPLICATIONS

AI+X Impacts

[08/12/24] 기계인공지능 신진연구자 심포지엄

Prof. Lee gives a seminar on "Physics-guided and Mechanism-inspired AI for Physical Consistency, Generalizability, and Interpretability"

(기계인공지능 신진연구자 심포지엄, 대한기계학회 기계인공지능연구회)