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

• URL: http://arxiv.org/abs/2606.11605v1
• Date: Wed, 10 Jun 2026 03:05:42 GMT
• Title: Physics-Distilled Neural Network enabled by Large Language Models for Manufacturing Process-Property Predictive Modeling
• Abstract summary: Predicting process-property relationships in manufacturing is often challenged by high experimental costs and the limited interpretability of complex 'black-box' models.<n>This paper proposes a novel knowledge distillation framework designed to achieve high-accuracy predictions in data-scarce scenarios.
• Score: 8.459710888681117
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 privileged teacher model. We employ a Graph-Masked Attention layer to capture the complex physical dependencies among input variables showing strict setpoints or a combination of static and high-frequency temporal signatures. This privileged knowledge is distilled into a lightweight student predictor for inference. The feasibility and robustness of the framework are evaluated through a comprehensive experiment across five diverse manufacturing processes. To ensure statistical reliability, given the small dataset sizes, a repeated K-fold cross-validation technique is employed to quantify model stability and generalization. Results indicate that the proposed framework consistently achieves high predictive accuracy across all evaluated domains. Most importantly, the architecture demonstrates significant fault tolerance by maintaining robust predictive performance even in scenarios where LLM-derived analytical priors are suboptimal or incomplete. Furthermore, the student predictor achieves an inference frequency exceeding 6000 Hz, which facilitates real-time edge deployment on standard industrial hardware. This work provides a scalable solution for bridging the gap between theoretical physics and real-time industrial monitoring in data-limited environments.

Related papers

• CLAIRE: Compressed Latent Autoencoder for Industrial Representation and Evaluation -- A Deep Learning Framework for Smart Manufacturing [51.56484100374058]
  CLAIRE is a hybrid end-to-end learning framework that integrates unsupervised deep representation learning with supervised classification for intelligent quality control in smart manufacturing systems.<n>It employs an optimized deep autoencoder to transform raw input into a compact latent space, effectively capturing the intrinsic data structure while suppressing irrelevant or noisy features.<n>The proposed framework highlights the potential of integrating explainable AI with feature-aware regularization for robust fault detection.
  arXiv  Detail & Related papers  (2026-03-06T15:11:58Z)
• Neural Additive Experts: Context-Gated Experts for Controllable Model Additivity [45.48194499967696]
  We propose a novel framework that seamlessly balances interpretability and accuracy.<n>Neural Additive Experts (NAEs) employ a mixture of experts framework, learning multiple specialized networks per feature.<n>We show that NAEs achieve an optimal balance between predictive accuracy and transparent, feature-level explanations.
  arXiv  Detail & Related papers  (2026-02-11T07:19:25Z)
• Demystifying Data-Driven Probabilistic Medium-Range Weather Forecasting [63.8116386935854]
  We demonstrate that state-of-the-art probabilistic skill requires neither intricate architectural constraints nor specialized trainings.<n>We introduce a scalable framework for learning multi-scale atmospheric dynamics by combining a directly downsampled latent space with a history-conditioned local projector.<n>We find that our framework design is robust to the choice of probabilistic estimators, seamlessly supporting interpolants, diffusion models, and CRPS-based ensemble training.
  arXiv  Detail & Related papers  (2026-01-26T03:52:16Z)
• Quantum-Aware Generative AI for Materials Discovery: A Framework for Robust Exploration Beyond DFT Biases [0.0]
  We introduce a quantum-aware generative AI framework for materials discovery.<n>We implement a robust active learning loop that quantifies and targets the divergence between low- and high-fidelity predictions.<n>Our results demonstrate a 3-5x improvement in successfully identifying potentially stable candidates in high-divergence regions.
  arXiv  Detail & Related papers  (2025-12-13T11:17:21Z)
• Opening the Black Box: An Explainable, Few-shot AI4E Framework Informed by Physics and Expert Knowledge for Materials Engineering [5.815650691228101]
  We present an explainable, few-shot AI4E framework that is systematically informed by physics and expert knowledge throughout its architecture.<n>Our approach provides a blueprint for developing AI systems that embed engineering domain knowledge directly into their architecture.
  arXiv  Detail & Related papers  (2025-11-28T06:50:29Z)
• Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction [20.1863553357121]
  Current deep learning architectures for remote sensing are fundamentally rigid.<n>We introduce the Spatial-Temporal-Spectral Unified Network (STSUN) for unified modeling.<n> STSUN can adapt to input and output data with arbitrary spatial sizes, temporal lengths, and spectral bands.<n>It unifies various dense prediction tasks and diverse semantic class predictions.
  arXiv  Detail & Related papers  (2025-05-18T07:39:17Z)
• Quantifying Robustness: A Benchmarking Framework for Deep Learning Forecasting in Cyber-Physical Systems [44.61435605872856]
  We introduce a practical robustness definition grounded in distributional robustness, explicitly tailored to industrial CPS.<n>Our framework simulates realistic disturbances, such as sensor drift, noise and irregular sampling, enabling thorough robustness analyses of forecasting models.
  arXiv  Detail & Related papers  (2025-04-04T14:50:48Z)
• Meta-Statistical Learning: Supervised Learning of Statistical Inference [59.463430294611626]
  This work demonstrates that the tools and principles driving the success of large language models (LLMs) can be repurposed to tackle distribution-level tasks.<n>We propose meta-statistical learning, a framework inspired by multi-instance learning that reformulates statistical inference tasks as supervised learning problems.
  arXiv  Detail & Related papers  (2025-02-17T18:04:39Z)
• Remaining Useful Life Prediction: A Study on Multidimensional Industrial Signal Processing and Efficient Transfer Learning Based on Large Language Models [6.118896920507198]
  This paper introduces an innovative regression framework utilizing large language models (LLMs) for RUL prediction. Experiments on the Turbofan engine's RUL prediction task show that the proposed model surpasses state-of-the-art (SOTA) methods. With minimal target domain data for fine-tuning, the model outperforms SOTA methods trained on full target domain data.
  arXiv  Detail & Related papers  (2024-10-04T04:21:53Z)
• On the Predictive Accuracy of Neural Temporal Point Process Models for Continuous-time Event Data [3.13468877208035]
  Temporal Point Processes (TPPs) serve as the standard mathematical framework for modeling asynchronous event sequences in continuous time. Researchers have proposed Neural TPPs, which leverage neural network parametrizations to offer more flexible and efficient modeling. This study systematically evaluates the predictive accuracy of state-of-the-art neural TPP models.
  arXiv  Detail & Related papers  (2023-06-29T16:14:43Z)
• Leveraging the structure of dynamical systems for data-driven modeling [111.45324708884813]
  We consider the impact of the training set and its structure on the quality of the long-term prediction. We show how an informed design of the training set, based on invariants of the system and the structure of the underlying attractor, significantly improves the resulting models.
  arXiv  Detail & Related papers  (2021-12-15T20:09:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.