Siamese Neural Network for Label-Efficient Critical Phenomena Prediction in 3D Percolation Models
arXiv:2507.14159v2 Announce Type: replace-cross Abstract: Predicting critical phenomena from limited labeled data remains a challenging task in statistical physics. As percolation theory provides a canonical model for phase transitions with well-established critical exponents, it serves as an ideal benchmark for validating new machine learning frameworks. Here, we introduce a label-efficient learning framework based on a Siamese Neural Network (SNN) to identify phase transitions in three-dimensional site and bond percolation models. Using only 22 labeled probability points drawn entirely from
The continuous advancements in AI research, particularly in neural networks and label-efficient learning, facilitate new approaches to complex scientific problems like critical phenomena prediction.
This development could significantly accelerate scientific discovery by enabling more efficient analysis of complex systems, potentially impacting materials science, drug discovery, and other fields reliant on understanding phase transitions with limited data.
The ability to predict critical phenomena with significantly less labeled data through advanced AI models like Siamese Neural Networks changes the efficiency and feasibility of research in statistical physics and related domains.
- · AI researchers
- · Materials science
- · Drug discovery
- · Computational physics
- · Traditional experimental methods requiring extensive labeling
More accurate and faster identification of critical points in various physical systems using reduced data overhead.
Accelerated development of new materials and therapeutics by providing better predictive tools for their properties and phase transitions.
Potential for entirely new paradigms in scientific exploration where complex systems can be understood and manipulated with unprecedented efficiency through AI-driven data economization.
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Read at arXiv cs.LG