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

This perspective highlights the need for advanced AI-driven inverse design systems in manufacturing that integrate domain knowledge, physics-informed learning, and human-AI interfaces to overcome challenges like sparse data and complex physical constraints. It emphasizes strategies such as expert-guided sampling and physics-based modeling to improve data efficiency, model accuracy, and the development of interactive, interpretable design ecosystems.