# Physical Rule-Guided Convolutional Neural Network

arXiv: https://arxiv.org/abs/2409.02081 · HTML: https://mlsanity.com/paper/2409.02081/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 21 references that are in the map.
- First posted on arXiv 3 Sep 2024.

## Builds on (most cited first, 9 of 9)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [U2-Net: Going Deeper with Nested U-Structure for Salient Object Detection](https://mlsanity.com/paper/2005.09007/) (2020)
- [Physics-Informed Computer Vision: A Review and Perspectives](https://mlsanity.com/paper/2305.18035/) (2023)
- [DOCK: Detecting Objects by transferring Common-sense Knowledge](https://mlsanity.com/paper/1804.01077/) (2018)
- [Physics-Guided Neural Networks for Intraventricular Vector Flow Mapping](https://mlsanity.com/paper/2403.13040/) (2024)
- [PgNN: Physics-guided Neural Network for Fourier Ptychographic Microscopy](https://mlsanity.com/paper/1909.08869/) (2019)
- [High-risk Factor Prediction in Lung Cancer Using Thin CT Scans: An Attention-Enhanced Graph Convolutional Network Approach](https://mlsanity.com/paper/2308.14000/) (2023)
- [Generalizing Graph Convolutional Neural Networks with Edge-Variant Recursions on Graphs](https://mlsanity.com/paper/1903.01298/) (2019)

## Abstract (arXiv)

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.