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Showing 1–6 of 6 results
Advanced filters: Author: Timoteo Carletti Clear advanced filters
  • Theoretical aspects of automated learning from data involving deep neural networks have open questions. Here Giambagli et al. show that training the neural networks in the spectral domain of the network coupling matrices can reduce the amount of learning parameters and improve the pre-training process.

    • Lorenzo Giambagli
    • Lorenzo Buffoni
    • Duccio Fanelli
    ResearchOpen Access
    Nature Communications
    Volume: 12, P: 1-9
  • This work explores the emergence and stability of global synchronization in Matrix Weighted Networks through the study of coupled Stuart-Landau oscillators. It provides a theoretical basis for the analysis of multidimensional dynamical processes in complex networked systems.

    • Anna Gallo
    • Yu Tian
    • Timoteo Carletti
    ResearchOpen Access
    Communications Physics
    Volume: 8, P: 1-13
  • Higher-order interactions reveal new aspects of the interplay between topology and dynamics in complex systems. This Perspective describes the emerging field of higher-order topological dynamics and discusses the open research questions in the area.

    • Ana P. Millán
    • Hanlin Sun
    • Ginestra Bianconi
    Reviews
    Nature Physics
    Volume: 21, P: 353-361
  • There is increasing evidence that many higher-order interactions in complex systems are directed. Here, the authors provide a tensorial formalism for directed hypergraphs and investigate their synchronization properties generalizing the Master Stability Function approach.

    • Luca Gallo
    • Riccardo Muolo
    • Timoteo Carletti
    ResearchOpen Access
    Communications Physics
    Volume: 5, P: 1-11