@book{RosenblumKurths1995,
  author    = {Rosenblum, Michael and Kurths, J{\"u}rgen},
  title     = {A model of neural control of heart rate},
  series = {Preprint NLD},
  volume    = {12},
  journal   = {Preprint NLD},
  publisher = {Univ.},
  address   = {Potsdam},
  pages     = {22 S.},
  year      = {1995},
  language  = {en}
}
@article{OzturkMalikCheungetal.2019,
  author    = {Ozturk, Ugur and Malik, Nishant and Cheung, Kevin and Marwan, Norbert and Kurths, J{\"u}rgen},
  title     = {A network-based comparative study of extreme tropical and frontal storm rainfall over Japan},
  series = {Climate dynamics : observational, theoretical and computational research on the climate system},
  volume    = {53},
  journal   = {Climate dynamics : observational, theoretical and computational research on the climate system},
  number    = {1-2},
  publisher = {Springer},
  address   = {New York},
  issn      = {0930-7575},
  doi       = {10.1007/s00382-018-4597-1},
  pages     = {521 -- 532},
  year      = {2019},
  abstract  = {Frequent and intense rainfall events demand innovative techniques to better predict the extreme rainfall dynamics. This task requires essentially the assessment of the basic types of atmospheric processes that trigger extreme rainfall, and then to examine the differences between those processes, which may help to identify key patterns to improve predictive algorithms. We employ tools from network theory to compare the spatial features of extreme rainfall over the Japanese archipelago and surrounding areas caused by two atmospheric processes: the Baiu front, which occurs mainly in June and July (JJ), and the tropical storms from August to November (ASON). We infer from complex networks of satellite-derived rainfall data, which are based on the nonlinear correlation measure of event synchronization. We compare the spatial scales involved in both systems and identify different regions which receive rainfall due to the large spatial scale of the Baiu and tropical storm systems. We observed that the spatial scales involved in the Baiu driven rainfall extremes, including the synoptic processes behind the frontal development, are larger than tropical storms, which even have long tracks during extratropical transitions. We further delineate regions of coherent rainfall during the two seasons based on network communities, identifying the horizontal (east-west) rainfall bands during JJ over the Japanese archipelago, while during ASON these bands align with the island arc of Japan.},
  language  = {en}
}
@article{GoswamiShekatkarRheinwaltetal.2015,
  author    = {Goswami, Bedartha and Shekatkar, Snehal M. and Rheinwalt, Aljoscha and Ambika, G. and Kurths, J{\"u}rgen},
  title     = {A random interacting network model for complex networks},
  series = {Scientific reports},
  volume    = {5},
  journal   = {Scientific reports},
  publisher = {Nature Publ. Group},
  address   = {London},
  issn      = {2045-2322},
  doi       = {10.1038/srep18183},
  pages     = {10},
  year      = {2015},
  abstract  = {We propose a RAndom Interacting Network (RAIN) model to study the interactions between a pair of complex networks. The model involves two major steps: (i) the selection of a pair of nodes, one from each network, based on intra-network node-based characteristics, and (ii) the placement of a link between selected nodes based on the similarity of their relative importance in their respective networks. Node selection is based on a selection fitness function and node linkage is based on a linkage probability defined on the linkage scores of nodes. The model allows us to relate within-network characteristics to between-network structure. We apply the model to the interaction between the USA and Schengen airline transportation networks (ATNs). Our results indicate that two mechanisms: degree-based preferential node selection and degree-assortative link placement are necessary to replicate the observed inter-network degree distributions as well as the observed inter-network assortativity. The RAIN model offers the possibility to test multiple hypotheses regarding the mechanisms underlying network interactions. It can also incorporate complex interaction topologies. Furthermore, the framework of the RAIN model is general and can be potentially adapted to various real-world complex systems.},
  language  = {en}
}
@article{PereiraBaptistaReyesetal.2009,
  author    = {Pereira, Tiago and Baptista, Murilo da Silva and Reyes, Marcelo B. and Caldas, Ibere Luiz and Sartorelli, Jos{\´e} Carlos and Kurths, J{\"u}rgen},
  title     = {A scenario for torus T-2 destruction via a global bifurcation},
  issn      = {0960-0779},
  doi       = {10.1016/j.chaos.2007.06.115},
  year      = {2009},
  abstract  = {We show a scenario of a two-frequeney torus breakdown, in which a global bifurcation occurs due to the collision of a quasi-periodic torus T-2 with saddle points, creating a heteroclinic saddle connection. We analyze the geometry of this torus-saddle collision by showing the local dynamics and the invariant manifolds (global dynamics) of the saddle points. Moreover, we present detailed evidences of a heteroclinic saddle-focus orbit responsible for the type- if intermittency induced by this global bifurcation. We also characterize this transition to chaos by measuring the Lyapunov exponents and the scaling laws.},
  language  = {en}
}
@misc{GoswamiBoersRheinwaltetal.2018,
  author    = {Goswami, Bedartha and Boers, Niklas and Rheinwalt, Aljoscha and Marwan, Norbert and Heitzig, Jobst and Breitenbach, Sebastian Franz Martin and Kurths, J{\"u}rgen},
  title     = {Abrupt transitions in time series with uncertainties},
  series = {Postprints der Universit{\"a}t Potsdam Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam Mathematisch-Naturwissenschaftliche Reihe},
  number    = {576},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-42311},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-423111},
  pages     = {10},
  year      = {2018},
  abstract  = {Identifying abrupt transitions is a key question in various disciplines. Existing transition detection methods, however, do not rigorously account for time series uncertainties, often neglecting them altogether or assuming them to be independent and qualitatively similar. Here, we introduce a novel approach suited to handle uncertainties by representing the time series as a time-ordered sequence of probability density functions. We show how to detect abrupt transitions in such a sequence using the community structure of networks representing probabilities of recurrence. Using our approach, we detect transitions in global stock indices related to well-known periods of politico-economic volatility. We further uncover transitions in the El Nino-Southern Oscillation which coincide with periods of phase locking with the Pacific Decadal Oscillation. Finally, we provide for the first time an 'uncertainty-aware' framework which validates the hypothesis that ice-rafting events in the North Atlantic during the Holocene were synchronous with a weakened Asian summer monsoon.},
  language  = {en}
}
@article{GoswamiBoersRheinwaltetal.2018,
  author    = {Goswami, Bedartha and Boers, Niklas and Rheinwalt, Aljoscha and Marwan, Norbert and Heitzig, Jobst and Breitenbach, Sebastian Franz Martin and Kurths, J{\"u}rgen},
  title     = {Abrupt transitions in time series with uncertainties},
  series = {Nature Communications},
  volume    = {9},
  journal   = {Nature Communications},
  publisher = {Nature Publ. Group},
  address   = {London},
  issn      = {2041-1723},
  doi       = {10.1038/s41467-017-02456-6},
  pages     = {10},
  year      = {2018},
  abstract  = {Identifying abrupt transitions is a key question in various disciplines. Existing transition detection methods, however, do not rigorously account for time series uncertainties, often neglecting them altogether or assuming them to be independent and qualitatively similar. Here, we introduce a novel approach suited to handle uncertainties by representing the time series as a time-ordered sequence of probability density functions. We show how to detect abrupt transitions in such a sequence using the community structure of networks representing probabilities of recurrence. Using our approach, we detect transitions in global stock indices related to well-known periods of politico-economic volatility. We further uncover transitions in the El Ni{\~n}o-Southern Oscillation which coincide with periods of phase locking with the Pacific Decadal Oscillation. Finally, we provide for the first time an 'uncertainty-aware' framework which validates the hypothesis that ice-rafting events in the North Atlantic during the Holocene were synchronous with a weakened Asian summer monsoon.},
  language  = {en}
}
@article{ZaikinKurths2000,
  author    = {Zaikin, Alexei A. and Kurths, J{\"u}rgen},
  title     = {Additive noise and noise-induced nonequilibrium phase transitions},
  isbn      = {1-563-96826-6},
  year      = {2000},
  language  = {en}
}
@article{ZaikinKurths2001,
  author    = {Zaikin, Alexei A. and Kurths, J{\"u}rgen},
  title     = {Additive noise in noise-induced nonequilibrium transitions},
  issn      = {1054-1500},
  year      = {2001},
  language  = {en}
}
@article{PortaDiRienzoWesseletal.2009,
  author    = {Porta, Alberto and Di Rienzo, Marco and Wessel, Niels and Kurths, J{\"u}rgen},
  title     = {Addressing the complexity of cardiovascular regulation},
  issn      = {1364-503X},
  doi       = {10.1098/rsta.2008.0292},
  year      = {2009},
  language  = {en}
}
@article{ParkRosenblumKurthsetal.1999,
  author    = {Park, Eun Hyoung and Rosenblum, Michael and Kurths, J{\"u}rgen and Zaks, Michael A.},
  title     = {Alternating locking ratios in imperfect phase synchronization},
  year      = {1999},
  language  = {en}
}