Physics-Distilled Neural Network enabled by Large Language Models for Manufacturing Process-Property Predictive Modeling

📰 ArXiv cs.AI

Learn how to apply physics-distilled neural networks using large language models for predictive modeling in manufacturing, improving accuracy in data-scarce scenarios

advanced Published 11 Jun 2026

Action Steps

Extract analytical physics priors from scientific literature using Large Language Models
Integrate extracted priors into a neural network architecture
Train the neural network using limited available data
Evaluate the model's performance on unseen data
Refine the model by incorporating additional physics priors or adjusting hyperparameters

Who Needs to Know This

Data scientists and manufacturing engineers can benefit from this approach to improve process-property predictive modeling, enhancing decision-making and reducing experimental costs

Key Insight

💡 Integrating physics priors extracted via LLMs into neural networks can enhance predictive accuracy in data-scarce manufacturing scenarios

Key Takeaways

Learn how to apply physics-distilled neural networks using large language models for predictive modeling in manufacturing, improving accuracy in data-scarce scenarios

Full Article

Title: Physics-Distilled Neural Network enabled by Large Language Models for Manufacturing Process-Property Predictive Modeling

Abstract:
arXiv:2606.11605v1 Announce Type: cross Abstract: Predicting process-property relationships in manufacturing is often challenged by high experimental costs and the limited interpretability of complex 'black-box' models. This paper proposes a novel knowledge distillation framework designed to achieve high-accuracy predictions in data-scarce scenarios. The framework integrates analytical physics priors, which are systematically extracted from scientific literature via Large Language Models, into a

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