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Introduction to Focus Issue: Data-driven models and analysis of complex systems
Johann H Martínez1, Klaus Lehnertz2,3,4, Nicolás Rubido5
1Complex Systems Group and G.I.S.C, Universidad Rey Juan Carlos, Móstoles, 28933 Madrid, Spain.
Chaos (Woodbury, N.Y.)
|March 14, 2025
Summary
This Focus Issue showcases data-driven research in complex systems, spanning diverse fields from finance to neuroscience. Advanced methods like machine learning and persistent homology are revolutionizing our understanding of intricate system dynamics.
Area of Science:
- Complexity Science
- Data-Driven Research
Background:
- Complex systems research spans diverse fields including finance, climate, and neuroscience.
- Traditional methods often struggle with the intricate dynamics of these systems.
Purpose of the Study:
- To highlight recent advances in the study of complex systems.
- To emphasize the impact of data-driven research and novel methodologies.
Main Methods:
- Machine learning
- Higher-order correlations
- Control theory
- Information theory
- Time series analysis
- Persistent homology
Main Results:
- Summarizes 47 published works showcasing diverse applications of complex systems.
- Demonstrates the power of advanced analytical techniques in understanding system dynamics.
- Highlights breakthroughs in areas like financial markets, climate science, and biomedicine.
Conclusions:
- Data-driven approaches are revolutionizing the study of complex systems.
- Novel methodologies significantly enhance the understanding of intricate system dynamics.
- Future research will be driven by the intersection of complexity science and the digital data era.
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