Data Analysis for Complex Systems
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Signal and Data Analysis".
Deadline for manuscript submissions: 30 April 2026 | Viewed by 38
Special Issue Editor
Special Issue Information
Dear Colleagues,
Complex systems describe a wide range of dynamical processes, including biological, social, atmospheric, or financial systems. These systems can display intricate and often stochastic dynamics, including collective behaviors, phase transitions, cascading failures, and abrupt regime shifts. Such phenomena emerge from local interactions among many interdependent elements, giving rise to large-scale organization without the need for centralized control. Descriptions of complex systems have been developed through rich interdisciplinary approaches, ranging from statistical physics to computational methods, aimed at capturing patterns, structures, and dynamics across multiple scales, and often validated through data analysis. More recently, data-driven models have emerged as powerful alternatives, offering new insights into the dynamical processes that govern complex systems.
This Special Issue of Entropy invites contributions that advance the study of complex systems through innovative applications of data analysis. We welcome both theoretical and applied works that explore methodological developments in statistics, network science, machine learning, and agent-based modeling, with a particular focus on uncovering emergent phenomena and structural insights. Submissions may address statistical challenges unique to complex systems, propose novel algorithms, or apply data-driven models to real-world systems. Reviews summarizing recent progress in this interdisciplinary area are also encouraged. Our aim is to highlight cutting-edge research that deepens our understanding of these systems through data, and to promote tools that can be generalized across domains.
Dr. Ixandra Achitouv
Guest Editor
Manuscript Submission Information
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Keywords
- data-driven analysis in complex systems
- machine learning and complex systems
- stochastic propagation in networks
- chaos, criticality, and emerging phenomena
- data analysis and multi-scaled simulations
- noise and signal in complex systems
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