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Data-Driven Models and Analysis of Complex Systems

The 2021 Nobel prize in Physics emphasized the importance of studying complex systems to understand their emerging behaviors, including synchronization, chaos, and extreme events. These behaviors have been studied due to the fascinating patterns they create, possible catastrophic consequences, and the universality that bounds them. Despite coming from natural or man-made systems, these behaviors require no external or central control but depend on the structure of their interactions — the network. Moreover, with more precise data — often big data —, new models, methods, and machine learning techniques allow innovative studies of complex systems.

This Focus Issue welcomes manuscripts that present new insights into complex systems, derived from computational or data-driven models, and the development of new data analysis methods to characterize collective behaviors or network structures.

Guest Editors: Johann H. Martínez, Klaus Lehnertz, and Nicolás Rubido

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Aladin Crnkić; Vladimir Jaćimović
Diogo L. M. Souza; Enrique C. Gabrick; Paulo R. Protachevicz; Fernando S. Borges; José Trobia; Kelly C. Iarosz; Antonio M. Batista; Iberê L. Caldas; Ervin K. Lenzi
Maurizio Titz; Franz Kaiser; Johannes Kruse; Dirk Witthaut
Harihara Sudhan Kumar
Salam Rabindrajit Luwang; Anish Rai; Md. Nurujjaman; Om Prakash; Chittaranjan Hens
Alberto Isaac Aguilar-Hernández; David Michel Serrano-Solis; Wady A. Ríos-Herrera; José Fernando Zapata-Berruecos; Gloria Vilaclara; Gustavo Martínez-Mekler; Markus F. Müller
Valeri A. Makarov; Ricardo Muñoz-Arnaiz; Oscar Herreras; Julia Makarova
Pablo Rosillo-Rodes; Maxi San Miguel; David Sánchez
Malgorzata J. Krawczyk; Krzysztof Malarz
A. Zabaleta-Ortega; C. Masoller; L. Guzmán-Vargas
Per Sebastian Skardal; Juan G. Restrepo
Priya B. Jain; Tung T. Nguyen; Ján Mináč; Lyle E. Muller; Roberto C. Budzinski
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