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
All 12 students of the IAI Lab successfully delivered oral presentations at the KSME Fall Annual Conference (대한기계학회 추계학술대회).
- Donghyun: Language Model-driven Cross-modal Symbolic Regression for Discovery of Governing Equations
- Kiho: A Novel Approach of Continual Fine-Tuning for Preserving Domain Specialization of Language Models
- Seongmin: PINAS: A Novel Approach of Physics-informed Neural Architecture Search
- Dongwon: LLM-augmented Signal Detection and Interpretation via Dynamics-Aware Tokenization
- Minwoo: Multi-agent-based Domain-specialized LLM for Seamless Design and Manufacturing
- Hoonhyung: Deep Generative and Explainable Prompt-guided Latent Diffusion
- Yeongtae: DroneMorph: 3D Drone Shape Dataset for Design Optimization
- Hyeongbae: Physics-informed Approach for Posture-Adaptive Intrabody Potential Prediction
- Jaeryun: Physics-informed Deep Operator Learning for Vehicle-level Thermal-Stress Surrogate Model
- Jeongwoo: Physically-aligned Flow Matching for Learning Latent Trajectories of Transient Dynamics
- Yujin: Optimizable Path-guided Diffusion Model for Deep Generative Structures
- Changhyeon: Expert-embedded Language Model for Autoheuristic Optimization of Physics-informed Neural Networks