用大模型生成的物理规则增强卷积网络，提升小数据下的检测精度
Physical Rule-Guided Convolutional Neural Network
- 将大模型生成的物理规则作为可学习层嵌入CNN
- false positive率显著降低，真实检测置信度提升
- 适合数据少、需高可信度的科学计算场景
卷积神经网络（CNN）的黑箱特性及其对大规模标注数据的依赖，限制了其在标注数据稀缺的复杂领域中的应用。物理引导神经网络（PGNN）通过融合科学原理与现实知识，提升了模型的可解释性与效率。本文提出一种新型物理引导卷积神经网络（PGCNN）架构，将动态、可训练且自动化的大型语言模型（LLM）生成的广泛认可规则，以自定义层形式集成至模型中，以应对数据有限和置信度低的问题。PGCNN在多个数据集上进行了评估，表现优于基线CNN模型。关键改进包括显著降低误报率，并提高真实检测的置信度。结果表明，PGCNN具有提升CNN在更广应用场景中性能的潜力。
原文摘要 · Abstract (English)
The black-box nature of Convolutional Neural Networks (CNNs) and their reliance on large datasets limit their use in complex domains with limited labeled data. Physics-Guided Neural Networks (PGNNs) have emerged to address these limitations by integrating scientific principles and real-world knowledge, enhancing model interpretability and efficiency. This paper proposes a novel Physics-Guided CNN (PGCNN) architecture that incorporates dynamic, trainable, and automated LLM-generated, widely recognized rules integrated into the model as custom layers to address challenges like limited data and low confidence scores. The PGCNN is evaluated on multiple datasets, demonstrating superior performance compared to a baseline CNN model. Key improvements include a significant reduction in false positives and enhanced confidence scores for true detection. The results highlight the potential of PGCNNs to improve CNN performance for broader application areas.
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