Deniz Eroğlu

Eroğlu, Deniz

Name Variants

Deniz, Eroglu
Eroglu,D.
Eroğlu, DENIZ
DENIZ EROĞLU
Eroglu, Deniz
Eroglu D.
EROĞLU, DENIZ
Eroğlu,D.
Deniz EROĞLU
Eroğlu, D.
Eroğlu, Deniz
Deniz Eroğlu
E., Deniz
EROĞLU, Deniz
E.,Deniz
D. Eroğlu
Eroglu,Deniz
Eroğlu, Deniz

Job Title

Dr. Öğr. Üyesi

Email Address

deniz.eroglu@khas.edu.tr

ORCID ID

0000-0001-6725-6949

Scopus Author ID

37006533200

Full item page

Scholarly Output

19

Articles

16

Citation Count

105

Supervised Theses

3 Scholarly Output Search Results

Now showing 1 - 10 of 19

Citation Count: 13
Recurrence analysis of extreme event-like data
(COPERNICUS GESELLSCHAFT MBH, 2021) Banerjee, Abhirup; Goswami, Bedartha; Hirata, Yoshito; Eroğlu, Deniz; Merz, Bruno; Kurths, Juergen; Marwan, Norbert
The identification of recurrences at various time-scales in extreme event-like time series is challenging because of the rare occurrence of events which are separated by large temporal gaps. Most of the existing time series analysis techniques cannot be used to analyze an extreme event-like time series in its unaltered form. The study of the system dynamics by reconstruction of the phase space using the standard delay embedding method is not directly applicable to event-like time series as it assumes a Euclidean notion of distance between states in the phase space. The edit distance method is a novel approach that uses the point-process nature of events. We propose a modification of edit distance to analyze the dynamics of extreme event-like time series by incorporating a nonlinear function which takes into account the sparse distribution of extreme events and utilizes the physical significance of their temporal pattern. We apply the modified edit distance method to event-like data generated from point process as well as flood event series constructed from discharge data of the Mississippi River in the USA and compute their recurrence plots. From the recurrence analysis, we are able to quantify the deterministic properties of extreme event-like data. We also show that there is a significant serial dependency in the flood time series by using the random shuffle surrogate method.
Citation Count: 3
Collective dynamics of random Janus oscillator networks
(AMER PHYSICAL SOC, 2020) Peron, Thomas; Eroğlu, Deniz; Rodrigues, Francisco A.; Moreno, Yamir
Janus oscillators have been recently introduced as a remarkably simple phase oscillator model that exhibits nontrivial dynamical patterns-such as chimeras, explosive transitions, and asymmetry-induced synchronization-that were once observed only in specifically tailored models. Here we study ensembles of Janus oscillators coupled on large homogeneous and heterogeneous networks. By virtue of the Ott-Antonsen reduction scheme, we find that the rich dynamics of Janus oscillators persists in the thermodynamic limit of random regular, Erdos-Renyi, and scale-free random networks. We uncover for all these networks the coexistence between partially synchronized states and a multitude of solutions of a collective state we denominate as a breathing standing wave, which displays global oscillations. Furthermore, abrupt transitions of the global and local order parameters are observed for all topologies considered. Interestingly, only for scale-free networks, it is found that states displaying global oscillations vanish in the thermodynamic limit.
Generalized Synchronization: Master-Slave Relationship in Three Coupled Systems
(Kadir Has Üniversitesi, 2022) Doğan, Gizem; Eroglu, Deniz
Synchronization is an important phenomenon for complex, biological, and physical systems such as the brain, i.e., Parkinson’s disease, heart beating, hand-clapping, power grids, lasers, and many others. Intuitively, we can express synchronization as strong correlations between coupled systems. We can state two scenarios in this manner. One is synchronization between identical systems, which is called complete synchronization; the other is the synchronization between the non-identical systems, called generalized synchronization. In this thesis, initially, we considered the two coupled systems and calculated the critical coupling value for the generalized synchronization analytically. More precisely, the Lorenz system drives two R¨ossler systems. We investigated the critical coupling value for synchronization numerically. However, real-world examples are much more complex. The most straightforward case was the two coupled systems for the generalized synchronization, and next, we focus on three coupled systems. In particular, suppose that we have three coupled one Lorenz and two R¨ossler systems. In our example, the Lorenz system drives the first R¨ossler system, and first R¨ossler system drives the second R¨ossler system, and finally, the second R¨ossler system also drives the Lorenz system. We calculated the critical coupling of the whole system for generalized synchronization and analyzed the time series for each system.
Citation Count: 17
Multifaceted Dynamics of Janus Oscillator Networks
(Amer Physical Soc., 2019) Nicolaou, Zachary G.; Eroğlu, Deniz; Motter, Adilson E.
Recent research has led to the discovery of fundamental new phenomena in network synchronization including chimera states explosive synchronization and asymmetry-induced synchronization. Each of these phenomena has thus far been observed only in systems designed to exhibit that one phenomenon which raises the questions of whether they are mutually compatible and if so under what conditions they co-occur. Here we introduce a class of remarkably simple oscillator networks that concurrently exhibit all of these phenomena. The dynamical units consist of pairs of nonidentical phase oscillators which we refer to as Janus oscillators by analogy with Janus particles and the mythological figure from which their name is derived. In contrast to previous studies these networks exhibit (i) explosive synchronization with identical oscillators, (ii) extreme multistability of chimera states including traveling intermittent and bouncing chimeras, and (iii) asymmetry-induced synchronization in which synchronization is promoted by random oscillator heterogeneity. These networks also exhibit the previously unobserved possibility of inverted synchronization transitions in which a transition to a more synchronous state is induced by a reduction rather than an increase in the coupling strength. These various phenomena are shown to emerge under rather parsimonious conditions and even in locally connected ring topologies which has the potential to facilitate their use to control and manipulate synchronization in experiments.
Citation Count: 10
Emergent hypernetworks in weakly coupled oscillators
(Nature Portfolio, 2022) Nijholt, Eddie; Ocampo-Espindola, Jorge Luis; Eroglu, Deniz; Kiss, Istvan Z.; Pereira, Tiago
Networks of weakly coupled oscillators had a profound impact on our understanding of complex systems. Studies on model reconstruction from data have shown prevalent contributions from hypernetworks with triplet and higher interactions among oscillators, in spite that such models were originally defined as oscillator networks with pairwise interactions. Here, we show that hypernetworks can spontaneously emerge even in the presence of pairwise albeit nonlinear coupling given certain triplet frequency resonance conditions. The results are demonstrated in experiments with electrochemical oscillators and in simulations with integrate-and-fire neurons. By developing a comprehensive theory, we uncover the mechanism for emergent hypernetworks by identifying appearing and forbidden frequency resonant conditions. Furthermore, it is shown that microscopic linear (difference) coupling among units results in coupled mean fields, which have sufficient nonlinearity to facilitate hypernetworks. Our findings shed light on the apparent abundance of hypernetworks and provide a constructive way to predict and engineer their emergence.
The Effect of Link Modifications on Network Synchronization
(Kadir Has Üniversitesi, 2022) KIRAN, NARÇİÇEĞİ; Eroglu, Deniz
A major issue in studying complex network systems, such as neuroscience and power grids, is understanding the response of network dynamics to link modifications. The notion of network G(G, f, H) refers to di↵usively coupled identical oscillators, where isolated dynamics are chosen to be chaotic. As a consequence of the di↵usive nature, a globally synchronized state emerges as an invariant synchronization subspace, and it will be locally stable above critical coupling strength. Furthermore, the real part of the second minimum eigenvalue of the Laplacian matrix is inverse proportional to the critical coupling strength. Thus, we can use it to determine the synchronizability between two networks. Due to the asymmetry of the Laplacian matrix of a directed graph, adding directed links might cause a decrease in the real part of the second minimum eigenvalue of the Laplacian. If, after adding a link to a graph in a given network, the real part of the second minimum eigenvalue of the Laplacian matrix increases, it is called the enhancement of synchronization. Otherwise, it is called the hindrance of synchronization. In this research, we explore how the stability of synchronization at di↵usively coupled oscillators is a↵ected by link modifications for the networks created using particular motifs, i.e., cycle and star motifs. We consider a weakly connected directed graph consisting of two strongly connected components connected by directed link(s) (called cutset). We study the synchronization transitions in such networks when new directed link(s) between the components, in the opposite direction of the cutset, is added and strongly connects the whole network. We explore which properties of underlying graphs and their connected components may hinder or enhance the synchronization.
Network Reconstruction From Data
(Kadir Has Üniversitesi, 2023) Kement, İrem Topal; Eroğlu, Deniz
Güç şebekeleri, ekosistem, iklim, nöron ağları ve bir hastalığın küresel ölçekte yayılması gibi hayatımızın temel bileşenlerinin bir ortak noktası vardır: karmaşık ağlar üzerinde etkileşen dinamik birimler olarak modellenebilmeleri. Pek çok örnekte, karmaşık sistemlerden elde edilen veriler doğal bir ağ yapısını temsil eder veya sistem özünde ağ yapısında olmasa bile bir ağ gibi modellenebilir. Ağ dinamiğini bilmek, bu karmaşık sistemlerden istenen işlevselliği elde etmek, dolayısıyla gelecekteki durumunu tahmin etmek ve kontrol etmek için çok önemlidir. Örneğin beynimizdeki nöron ağlarının etkileşimindeki normal olmayan değişiklikler patolojik durumlara yol açabileceğinden, bu ağlar insan sağlığı için önemli bir dinamik ağ sınıfını oluştururlar. Epilepsi krizleri nöron ağlarının etkileşimlerinin değişmesi ile beliren ağ senkronizasyonu ile ilişkilidir. Bu tip istenmeyen nöronal senkronizasyona kritik geçişleri önceden tahmin etmek ve erken uyarı sinyallerini tespit edecek teknolojileri icat etmek hayati önem taşır. Nöronların iç dinamikleri ve aralarındaki bağlantı şemasından oluşan nöron ağlarında, senkronizasyona kritik geçiş doğrudan belirlenemez. Bu nedenle amaç, parametre değişikliklerinden kaynaklanan kritik geçişleri tahmin etmek için ağ dinamiğinin denklemini her bir düğümden elde edilen ölçüm verisinden öğrenmektir. Bu doktora çalışması, dinamik sistemler teorisinden ortalama alan yaklaşımlarını istatistiksel öğrenme araçlarıyla birleştirerek zaman serisi gözlemlerinden dinamik bir ağı yeniden yapılandırma yaklaşımı sunar. Önerilen veri güdümlü yeniden yapılandırma yaklaşımı iki temel varsayımda bulunur: sinirbilimsel bir model ve tüm düğümlerin verisine tam erişim. Buna karşılık, düğümlerin iç dinamikleri, aralarındaki bağlantı yapısı ve etkileşim şekli bilinmez. Sinirbilimsel koşullar, nöronların iç dinamiğinin kaotik davranış göstermesi, zayıf bir etkileşimde olmaları ve ölçekten bağımsız bir ağ ile temsil edilmeleri olarak sıralanır. Metodolojimiz tüm bilinmeyenleri nispeten kısa zaman serileri kullanarak doğru bir şekilde öğrenir ve ağ boyutundan bağımsızdır. Kısa süreli ölçüm ve büyük ağlarda başarı gerçek dünya örneklerine yaklaşabilmemiz için önemli iki kısıt olarak ele alınmıştır. Sonuç olarak, veriden öğrenilmiş ağ modeli tüm parametreleri kontrol edebilmemize ve karmaşık ağın kolektif davranışını tahmin edebilmemize izin verir.
Citation Count: 14
Revealing Dynamics, Communities, and Criticality From Data
(Amer Physical Soc, 2020) Eroğlu, Deniz; Tanzi, Matteo; van Strien, Sebastian; Pereira, Tiago
Complex systems such as ecological communities and neuron networks are essential parts of our everyday lives. These systems are composed of units which interact through intricate networks. The ability to predict sudden changes in the dynamics of these networks, known as critical transitions, from data is important to avert disastrous consequences of major disruptions. Predicting such changes is a major challenge as it requires forecasting the behavior for parameter ranges for which no data on the system are available. We address this issue for networks with weak individual interactions and chaotic local dynamics. We do this by building a model network, termed an effective network, consisting of the underlying local dynamics and a statistical description of their interactions. We show that behavior of such networks can be decomposed in terms of an emergent deterministic component and a fluctuation term. Traditionally, such fluctuations are filtered out. However, as we show, they are key to accessing the interaction structure. We illustrate this approach on synthetic time series of realistic neuronal interaction networks of the cat cerebral cortex and on experimental multivariate data of optoelectronic oscillators. We reconstruct the community structure by analyzing the stochastic fluctuations generated by the network and predict critical transitions for coupling parameters outside the observed range.
Citation Count: 0
Network Dynamics Reconstruction From Data
(Scıentıfıc Technıcal Research Councıl Turkey-Tubıtak, 2020) Eroğlu, Deniz
We consider the problem of recovering the model of a complex network of interacting dynamical units from time series of observations. We focus on typical networks which exhibit heterogeneous degrees, i.e. where the number of connections varies widely across the network, and the coupling strength for a single interaction is small. In these networks, the behavior of each unit varies according to their connectivity. Under these mild assumptions, our method provides an effective network reconstruction of the network dynamics. The method is robust to a certain size of noise and only requires relatively short time series on the state variable of most nodes to determine: how well-connected a particular node is, the distribution of the nodes' degrees in the network, and the underlying dynamics.
Citation Count: 6
Collective Dynamics of Random Janus Oscillator Networks
(American Physical Society, 2020) Peron,T.; Eroglu,D.; Rodrigues,F.A.; Moreno,Y.
Janus oscillators have been recently introduced as a remarkably simple phase oscillator model that exhibits nontrivial dynamical patterns-such as chimeras, explosive transitions, and asymmetry-induced synchronization-that were once observed only in specifically tailored models. Here we study ensembles of Janus oscillators coupled on large homogeneous and heterogeneous networks. By virtue of the Ott-Antonsen reduction scheme, we find that the rich dynamics of Janus oscillators persists in the thermodynamic limit of random regular, Erdos-Rényi, and scale-free random networks. We uncover for all these networks the coexistence between partially synchronized states and a multitude of solutions of a collective state we denominate as a breathing standing wave, which displays global oscillations. Furthermore, abrupt transitions of the global and local order parameters are observed for all topologies considered. Interestingly, only for scale-free networks, it is found that states displaying global oscillations vanish in the thermodynamic limit. © 2020 authors. Published by the American Physical Society.