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Cited by 299

The DOI matches its Crossref record; 387 of the 387 references it cites were found in a registry or an index.

Data based identification and prediction of nonlinear and complex dynamical systems

Open access
Wang, Wen-Xu, Lai, Ying-Cheng, Grebogi, Celso·2016·Physics Reports
DOI 10.1016/j.physrep.2016.06.004
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Abstract

The problem of reconstructing nonlinear and complex dynamical systems from measured data or time series is central to many scientific disciplines including physical, biological, computer, and social sciences, as well as engineering and economics. In this paper, we review the recent advances in this forefront and rapidly evolving field, aiming to cover topics such as compressive sensing (a novel optimization paradigm for sparse-signal reconstruction), noised-induced dynamical mapping, perturbations, reverse engineering, synchronization, inner composition alignment, global silencing, Granger Causality and alternative optimization algorithms. Often, these rely on various concepts from statistical and nonlinear physics such as phase transitions, bifurcation, stabilities, and robustness. The methodologies have the potential to significantly improve our ability to understand a variety of complex dynamical systems ranging from gene regulatory systems to social networks towards the ultimate goal of controlling such systems. Despite recent progress, many challenges remain. A purpose of this Review is then to point out the specific difficulties as they arise from different contexts, so as to stimulate further efforts in this interdisciplinary field.

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Metadata

Authors
Wang, W.-X., Lai, Y.-C., & Grebogi, C.
Venue
Physics Reports
Publisher
Elsevier BV
Published
2016-07
Volume
644
Pages
1-76
ISSN
0370-1573 (ISSN-L)
DOI
10.1016/j.physrep.2016.06.004
OpenAlex
W2475109240
Cited by
299
References
387
FWCI
17.87
Type
journal article
Language
en
Open access
bronze

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