标题： 面向制造中逆向设计的知识引导人工智能：领域、物理和人机协同的视角 显示英文标题

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

摘要： 人工智能（AI）正在重塑制造业中的逆向设计，使材料、产品和工艺的高性能发现成为可能。 然而，在数据稀疏、高维设计空间和复杂约束的现实制造环境中，纯粹的数据驱动方法往往面临困难。 本文提出了一种基于三个互补支柱的综合框架：领域知识用于建立具有物理意义的目标和约束，同时去除相关性有限的变量；基于物理信息的机器学习在数据有限或有偏的情况下提高泛化能力；基于大语言模型的界面支持直观的人机交互。 以注塑成型为例，我们展示了这些组件如何在实际中运行，并通过强调在现实制造环境中应用此类方法的关键挑战来结束。 显示英文摘要

摘要： Artificial intelligence (AI) is reshaping inverse design in manufacturing, enabling high-performance discovery in materials, products, and processes. However, purely data-driven approaches often struggle in realistic manufacturing settings characterized by sparse data, high-dimensional design spaces, and complex constraints. This perspective proposes an integrated framework built on three complementary pillars: domain knowledge to establish physically meaningful objectives and constraints while removing variables with limited relevance, physics-informed machine learning to enhance generalization under limited or biased data, and large language model-based interfaces to support intuitive, human-centered interaction. Using injection molding as an illustrative example, we demonstrate how these components can operate in practice and conclude by highlighting key challenges for applying such approaches in realistic manufacturing environments.