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- W4324352288 abstract "Reconstructing network dynamics from data is crucial for predicting the changes in the dynamics of complex systems such as neuron networks; however, previous research has shown that the reconstruction is possible under strong constraints such as the need for lengthy data or small system size. Here, we present a recovery scheme blending theoretical model reduction and sparse recovery to identify the governing equations and the interactions of weakly coupled chaotic maps on complex networks, easing unrealistic constraints for real-world applications. Learning dynamics and connectivity lead to detecting critical transitions for parameter changes. We apply our technique to realistic neuronal systems with and without noise on a real mouse neocortex and artificial networks." @default.
- W4324352288 created "2023-03-16" @default.
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- W4324352288 date "2023-03-15" @default.
- W4324352288 modified "2023-09-26" @default.
- W4324352288 title "Reconstructing Network Dynamics of Coupled Discrete Chaotic Units from Data" @default.
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- W4324352288 doi "https://doi.org/10.1103/physrevlett.130.117401" @default.
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