Introduction

This repository contains the codes to early predictor for the onset of critical transitions in networked dynamical systems.

If you use anything in this repository, then please cite:

Zijia Liu, Xiaozhu Zhang, Xiaolei Ru, Ting-Ting Gao, Jack Murdoch Moore, and Gang Yan, Early Predictor for the Onset of Critical Transitions in Networked Dynamical Systems, Physical Review X, 2024

This paper has received prominent recognition, having been featured in Nature Physics through Dr. Karen Mudryk’s article titled "Precise Precognition". Additionally, the American Physical Society (APS) has selected it for highlighting, with Prof. Naoki Masuda penning a captivating viewpoint entitled "Predicting Tipping Points in Complex Systems".

Files

• biomass_data_generating.py - File to generate data for resource biomass systems.
• biomass_prediction.py - File for predicting the critical time of sharp decline of resource biomass.
• GNN_RNNmodel.py - File of our GIN-GRU deep learning neural network architecture.
• NPRT_HN.py - File to test the robustness of our approach against different fractions of incomplete data.
• NPRT_SNR.py - File to test the robustness of our approach against different SNR (dB) of observational noise.
• transient_conti_generating.py - File to generate data for the circumstance in which the control parameter continuously increases.
• transient_prediction.py - File to test the robustness of our approach against transient data.
• CTPiCS.py - File to pre-train joint model on massive synthetic data of three systems.
• CTPiCS2real.py - File to fine-tune joint model and then predict a new empirical system.

Folder

• empirical_data - Data of a new empirical system.