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Updated: Jun 18, 2025

Setting Limits on Supersymmetry Using Simplified Models
Published on: November 15, 2013
A simplex path integral and a simplex renormalization group for high-order interactions.
Aohua Cheng1,2,3, Yunhui Xu4, Pei Sun5,6
1Department of Psychological and Cognitive Sciences, Tsinghua University, Beijing 100084, People's Republic of China.
We introduce simplex renormalization groups (SRGs) to analyze complex systems with high-order interactions. This new method generalizes path integrals and renormalization groups for better characterization of universality in intricate systems.
Area of Science:
- Complex Systems Science
- Statistical Physics
- Network Theory
Background:
- Path integrals and renormalization groups (RGs) are foundational for phase transitions and scale invariance.
- Classic RG methods are limited in complex systems with high-order, undecomposable interactions.
Purpose of the Study:
- To generalize path integral formulation and RG methods for systems with arbitrary high-order and heterogeneous interactions.
- To develop a framework for characterizing universality in complex systems with high-order interactions.
Main Methods:
- Formalization of unit trajectories under high-order interactions.
- Development of simplex path integrals and simplex RG (SRG) using high-order propagators.
- Momentum-space integration of short-range interactions and coarse-graining on simplex structures.
- Implementation of a divide-and-conquer framework within SRG to handle non-ergodicity and inter-order renormalization (p <= q).
Main Results:
- The proposed SRG effectively analyzes systems with high-order interactions, overcoming limitations of classic RG.
- Associated scaling relations differentiate between scale-invariant, weakly scale-invariant, and scale-dependent systems.
- Validation across diverse applications including scale-invariance verification, topological discovery, and information bottleneck analysis.
Conclusions:
- The simplex RG provides a robust theoretical tool for understanding complex systems with high-order interactions.
- The framework accurately identifies intrinsic statistical and topological properties during system reduction.
- This generalization enhances the analysis of universality across various scientific domains.
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