Neural Network Implementation of the Renormalization Group for Fault Diagnosis with Class Imbalance

• URL: http://arxiv.org/abs/2606.18326v1
• Date: Tue, 16 Jun 2026 17:27:40 GMT
• Title: Neural Network Implementation of the Renormalization Group for Fault Diagnosis with Class Imbalance
• Abstract summary: This paper proposes RGNet, a neural network architecture based on the concept of the renormalization group (RG)<n>The model sequentially compresses the input dimensionality and classifications all scales before capturing both local details and global patterns.<n>The obtained results demonstrate that RGNet is a universal, interpretable, and competitive solution for fault prediction in applications with imbalanced classes.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The application of machine learning models in practical tasks faces challenges such as class imbalance and multidimensional noise. This paper proposes RGNet, a neural network architecture based on the concept of the renormalization group (RG), for hierarchical coarse-graining of the feature space. The model sequentially compresses the input dimensionality and concatenates all scales before classification, allowing it to capture both local details and global patterns. The notion of RG-flows is introduced - interpretable low-dimensional representations whose visualization via t-SNE reveals a discrete curvilinear structure confirming the effectiveness of coarse-graining. Experimental results are presented on the imbalanced AI4I dataset. The obtained results demonstrate that RGNet is a universal, interpretable, and competitive solution for fault prediction in applications with imbalanced classes.

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