- InnoCORE-LLM
- Platform for Real-world Innovation in Smart Manufacturing through AI (PRISM-AI)
- AI-CRED Institute(AI Co-Research & Education for Innovative Drug)
- AI-Transformed Aerospace Research Center
- Photonic artificial Intelligence COmputing REsearch Center
- Regenerative Medical Research Institute (reMRI) for Aging-related Diseases
- T-RAX: Transformative Railway with AX
- 5D AI-RI: 5D AI Robotics Initiative
- Artificial Cell Initiative
- POwer-autonomous Subsea Engineering for Intelligent Data-center Operating Networks
- Sustainable Materials Institute for Post-AI Era
- AI Meta-Scientist
Platform for Real-world Innovation in Smart Manufacturing through AI (PRISM-AI)
KEYWORDKeyword
OBJECTIVE Objectives
Advancing National AI Manufacturing Technology: Establishing integrated AI system technologies for design-material-manufacturing-operation using LLMs
Building a Foundation for Commercialization: Securing TRL 7 level technologies and promoting industry-wide demonstrations and technology transfers
Accelerating Industrial Digital Transformation: Removing barriers to AI adoption for SMEs and mid-sized enterprises to foster digital manufacturing
INTRODUCTION Director's Message
The PRISM-AI Research Group aims to solve complex challenges in design and manufacturing
by merging AI with engineering domain knowledge, creating tangible value for the industrial field.
Based on our world-class research capabilities and collaborative culture,
we will lead the innovation in AI-driven design and manufacturing
IMPACT Impact
Industry
Implementation of high-efficiency, low-cost production systems
→ Expected to improve yield by over 10%
and reduce design time by 50%
Society & Policy
Encouraging young talent to enter the manufacturing sector
and addressing the labor gap of skilled technicians
Education
Cultivating world-class AI+X talent
and establishing a foundation for global advancement
TALENT Ideal Talent
Problem-Solving Research Excellence : Ability to define complex engineering problems in design and manufacturing and solve them by integrating data, physics, and domain knowledge (Problem-driven approach)
AI-Engineering Convergence : Capability to develop data-efficient and generalizable AI manufacturing technologies
by combining AI (Machine Learning, Optimization, etc.) with engineering domain expertise
Collaboration & Industry-Impact Mindset : A research attitude focused on connecting academic achievements
to industrial innovation and social value through interdisciplinary and industry-academic collaboration