Toward Knowledge-Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human-AI Synergy

The article highlights advancements in AI-driven inverse design for manufacturing, emphasizing the integration of domain knowledge, physics-informed learning, and human-AI interfaces to overcome limitations of purely data-driven methods. Key innovations include expert-guided sampling for improved data efficiency and the use of physics-informed models to ensure physical consistency in data-scarce environments.