Reconstructing Network Dynamics of Coupled Discrete Chaotic Units from Data

Topal, I.; Eroğlu, Deniz

doi:10.1103/physrevlett.130.117401

Cited by 6 publications

(2 citation statements)

References 47 publications

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“…Additionally, this approximation of local dynamics is not fully disentangled from the interaction dynamics. An extension and formalisation of this approach was subsequently presented [37] and demonstrated the capacity for this approach in predicting emergence behaviour.…”

Section: Introductionmentioning

confidence: 99%

A Backpropagation Algorithm for Inferring Disentagled Nodal Dynamics and Connectivity Structure of Dynamical Networks

Tan,

Corrêa,

Stemler

et al. 2024

IEEE Trans. Netw. Sci. Eng.

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Dynamical networks are versatile models that describe a variety of behaviours such as synchronisation and feedback in networks of coupled dynamical components. However, applying these models in real systems is difficult as prior information of the connectivity structure or local dynamics is often unknown and must be inferred from node state observations. Additionally, the influence of coupling interactions complicates the isolation of local node dynamics. Given the architectural similarities between dynamical networks and recurrent neural networks (RNNs), we propose a network inference method based on the backpropagation through time (BPTT) algorithm used to train RNNs. This method aims to simultaneously infer both the connectivity structure and isolated local node dynamics from node state observations. An approximation of local node dynamics is first constructed using a neural network. This is alternated with an adapted BPTT algorithm to regress corresponding network weights by minimising prediction errors of the network based on the previously constructed local models until convergence. This method was successful in identifying the connectivity structure for coupled networks of chaotic oscillators. Freerun prediction performance with the resulting local models and weights was comparable to the true system with noisy initial conditions. The method is also extended to asymmetric negative coupling.

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Section: Introductionmentioning

confidence: 99%

A Backpropagation Algorithm for Inferring Disentagled Nodal Dynamics and Connectivity Structure of Dynamical Networks

Tan,

Corrêa,

Stemler

et al. 2024

IEEE Trans. Netw. Sci. Eng.

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“…Since the fast variable of the isolated map contains a rational function in Equation (5.1), it makes the reconstruction a non-trivial task. In fact, if one attempts to reconstruct the dynamics using only polynomials (viewing as a Taylor expansion around zero), the resulting model only predicts the data inside certain vicinity of zero (EROGLU et al, 2020;TOPAL;EROGLU, 2023), limiting its usage. So, we consider a class of network dynamics that can be represented by rational functions.…”

Section: Reconstruction Problemmentioning

confidence: 99%

Reconstruction of sparse network dynamics from data

Santos

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To all my old and new friends who accompanied me throughout this path. Directly or indirectly all of you offered something that I carried during these years. Undoubtedly, I am truly thankful.To my partner and friend, Larissa Regina Diniz, for her support, understanding, and love, and for providing a listening ear during moments of doubt. From COVID-19 quarantine to a long-distance relationship, we faced together different adventures. You definitely have a part in the conclusion of this thesis.Lastly, I am grateful to Edmilson dos Santos, Gabriela Roque dos Santos, and Rojanira Roque dos Santos, my family, for their constant encouragement, understanding, and love. My full gratitude for all their investment of time, patience, and support during not only the last five years but 21 and 29 years (hu3!), respectively. This thesis would not have been possible without their support. Thanks for being a part of this journey.Palavras-chave: Dinâmica de redes, redes esparsas, sistemas dinâmicos, teoria ergódica, métodos de recuperação esparsa.

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Deep-learning reconstruction of complex dynamical networks from incomplete data

Ding,

Kong,

Zhang

et al. 2024

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Reconstructing complex networks and predicting the dynamics are particularly challenging in real-world applications because the available information and data are incomplete. We develop a unified collaborative deep-learning framework consisting of three modules: network inference, state estimation, and dynamical learning. The complete network structure is first inferred and the states of the unobserved nodes are estimated, based on which the dynamical learning module is activated to determine the dynamical evolution rules. An alternating parameter updating strategy is deployed to improve the inference and prediction accuracy. Our framework outperforms baseline methods for synthetic and empirical networks hosting a variety of dynamical processes. A reciprocity emerges between network inference and dynamical prediction: better inference of network structure improves the accuracy of dynamical prediction, and vice versa. We demonstrate the superior performance of our framework on an influenza dataset consisting of 37 US States and a PM2.5 dataset covering 184 cities in China.

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Reconstructing Network Dynamics of Coupled Discrete Chaotic Units from Data

Cited by 6 publications

References 47 publications

A Backpropagation Algorithm for Inferring Disentagled Nodal Dynamics and Connectivity Structure of Dynamical Networks

A Backpropagation Algorithm for Inferring Disentagled Nodal Dynamics and Connectivity Structure of Dynamical Networks

Reconstruction of sparse network dynamics from data

Deep-learning reconstruction of complex dynamical networks from incomplete data

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