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

2025-05-29

AI Summary

In manufacturing reverse design, purely data-driven approaches fail due to sparse, high-dimensional, nonlinear data and stringent physical constraints. To address this, we propose a knowledge-guided reverse design paradigm integrating domain-expert priors, physics-informed machine learning (PIML), and a large language model (LLM)-driven interactive design agent—forming a multimodal human–AI collaboration framework. Experts guide efficient sampling; PIML embeds governing physical laws to enhance generalizability; and the LLM enables natural-language interaction and design-intent understanding. Compared to black-box modeling, our approach significantly improves modeling efficiency and interpretability under data scarcity, while supporting verifiable, collaborative, and deployable AI-augmented design systems. It establishes a novel pathway toward high-fidelity, low-data-dependency intelligent manufacturing reverse design.

Technology Category

• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions

Abstract

Artificial intelligence (AI) is reshaping inverse design across manufacturing domain, enabling high-performance discovery in materials, products, and processes. However, purely data-driven approaches often struggle in realistic settings characterized by sparse data, high-dimensional design spaces, and nontrivial physical constraints. This perspective argues for a new generation of design systems that transcend black-box modeling by integrating domain knowledge, physics-informed learning, and intuitive human-AI interfaces. We first demonstrate how expert-guided sampling strategies enhance data efficiency and model generalization. Next, we discuss how physics-informed machine learning enables physically consistent modeling in data-scarce regimes. Finally, we explore how large language models emerge as interactive design agents connecting user intent with simulation tools, optimization pipelines, and collaborative workflows. Through illustrative examples and conceptual frameworks, we advocate that inverse design in manufacturing should evolve into a unified ecosystem, where domain knowledge, physical priors, and adaptive reasoning collectively enable scalable, interpretable, and accessible AI-driven design systems.

Problem

• Overcoming sparse data and high-dimensional design spaces in manufacturing
• Integrating domain knowledge and physics into AI-driven design systems
• Enhancing human-AI synergy for interpretable and scalable inverse design

Innovation

• Expert-guided sampling enhances data efficiency
• Physics-informed learning ensures consistent modeling
• Large language models connect intent with tools