Toward Knowledge-Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human-AI Synergy
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 10 of its 55 references that are in the map. First posted on arXiv 29 May 2025.
Builds on
10 of 55 references.
References · 10
- 2019DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operatorscited by 2,018
- 2021Characterizing possible failure modes in physics-informed neural networkscited by 643
- 2021Learning the solution operator of parametric partial differential equations with physics-informed DeepOnetscited by 540
- 2021Physics-Informed Neural Operator for Learning Partial Differential Equationscited by 527
- 2024Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programscited by 291
- 2023"It's a Fair Game", or Is It? Examining How Users Navigate Disclosure Risks and Benefits When Using LLM-Based Conversational Agentscited by 60
- 2024AMGPT: a Large Language Model for Contextual Querying in Additive Manufacturingcited by 23
- 2024"I'm categorizing LLM as a productivity tool": Examining ethics of LLM use in HCI research practicescited by 14
- 2024Sketch2Prototype: Rapid Conceptual Design Exploration and Prototyping with Generative AIcited by 13
- 2024AI-driven inverse design of materials: Past, present and futurecited by 10
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Citing papers per year: 2026: 2.
2 papers.