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.
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.