融合领域知识、物理模型与人机协同的AI逆向设计框架
Toward Knowledge-Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human-AI Synergy
- 用领域知识筛选关键变量，构建物理可解释的设计目标
- 通过物理约束增强小样本数据下的模型泛化能力
- 用大语言模型实现直观的人机交互，适合制造领域研究者
人工智能正在重塑制造领域的逆向设计，助力材料、产品和工艺的高性能发现。然而，纯数据驱动方法在真实制造环境中常因数据稀疏、设计空间高维和复杂约束而受限。本文提出一个整合三大支柱的框架：利用领域知识建立物理合理的目标与约束，剔除无关变量；通过物理信息机器学习提升有限或有偏数据下的泛化性能；借助大语言模型构建直观的人机交互界面。以注塑成型为例，展示了各组件的实际协同机制，并指出了该类方法在真实制造场景中应用的关键挑战。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。