Eroğlu, Deniz
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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
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
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Molecular Biology and Genetics
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12
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9
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17
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11
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16
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Documents
36
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731
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16
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Scholarly Output
27
Articles
21
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2/0
Supervised MSc Theses
3
Supervised PhD Theses
3
WoS Citation Count
201
Scopus Citation Count
219
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8
Scopus h-index
8
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7.44
Scopus Citations per Publication
8.11
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20
Supervised Theses
6
| Journal | Count |
|---|---|
| Physical Review Research | 2 |
| Physical Review X | 2 |
| Earth Surface Processes and Landforms | 1 |
| Entropy | 1 |
| European Physical Journal-Special Topics | 1 |
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Now showing 1 - 10 of 27
Master Thesis Makroskopik Ağ Sistemlerinde Veri Odaklı Trafik Akışı Modellemesi(2025) Fırat, Toprak; Eroğlu, DenizKentsel trafik sıkışıklığı, günümüz şehirleri için süregelen, karmaşık ve yüksek maliyetli bir problemdir. Artan seyahat süreleri, çevresel bozulma, enerji israfı ve ekonomik kayıplar bu problemin doğrudan sonuçları arasında yer almaktadır. Bu sorunlarla etkili şekilde başa çıkabilmek yalnızca altyapı yatırımlarıyla değil; aynı zamanda ulaşım politikaları, trafik yönetimi ve kontrol sistemlerinin bilimsel temellerle tasarlanmasıyla mümkündür. Bu kapsamda, trafiğin zaman ve mekân içinde nasıl evrildiğine dair sistematik ve ölçeklenebilir bir anlayış geliştirmek kritik önem taşır. Gerçek dünyada yapılacak deneyler genellikle maliyetli, zaman alıcı ve bozucudur. Bu nedenle, kentsel trafik sistemlerinin modellenmesi; alternatif senaryoların test edilmesi, politika etkilerinin değerlendirilmesi ve uzun vadeli sonuçların öngörülebilmesi açısından vazgeçilmez bir araçtır. Bununla birlikte, mevcut trafik modelleme yaklaşımları önemli sınırlılıklar taşır. Mikroskobik modeller bireysel araç davranışlarını yüksek ayrıntıyla temsil etse de, büyük ağlarda hesaplama açısından verimsizdir ve yoğun kalibrasyon verisi gerektirir. Makroskobik modeller ise daha hesaplıdır; ancak sabit başlangıç-varış (OD) akışları, homojen yol davranışları ve sürekli akış varsayımları gibi sadeleştirici kabuller içerir. Bu da onları karmaşık ve heterojen şehir yapıları için yetersiz kılar. Bu tez, trafik akışını yönlü bir ağda ayrık zamanlı yük alışverişiyle temsil eden veri odaklı bir makroskobik model önermektedir. Yol türlerine özgü akış dinamikleri, ağ topolojisi ve gözlemlenen trafik yoğunlukları modele entegre edilerek darboğazlar, geri tepme ve yük yeniden dağılımı gibi olgular temsil edilmektedir. Model parametreleri, evrimsel optimizasyon yoluyla, örtük talep varsayımı olmadan veriden öğrenilmektedir. Model, klasik Hücresel İletim Modeli (CTM) ile karşılaştırılmış; SUMO simülasyonları ve İstanbul, Londra ile New York verileri üzerinde üstünlük göstermiştir.Article Citation - WoS: 8Citation - Scopus: 8Sampling Rate-Corrected Analysis of Irregularly Sampled Time Series(Amer Physical Soc, 2022) Braun, Tobias; Fernandez, Cinthya N.; Eroglu, Deniz; Hartland, Adam; Breitenbach, Sebastian F. M.; Marwan, NorbertThe analysis of irregularly sampled time series remains a challenging task requiring methods that account for continuous and abrupt changes of sampling resolution without introducing additional biases. The edit distance is an effective metric to quantitatively compare time series segments of unequal length by computing the cost of transforming one segment into the other. We show that transformation costs generally exhibit a nontrivial relationship with local sampling rate. If the sampling resolution undergoes strong variations, this effect impedes unbiased comparison between different time episodes. We study the impact of this effect on recurrence quantification analysis, a framework that is well suited for identifying regime shifts in nonlinear time series. A constrained randomization approach is put forward to correct for the biased recurrence quantification measures. This strategy involves the generation of a type of time series and time axis surrogates which we call sampling-rate-constrained (SRC) surrogates. We demonstrate the effectiveness of the proposed approach with a synthetic example and an irregularly sampled speleothem proxy record from Niue island in the central tropical Pacific. Application of the proposed correction scheme identifies a spurious transition that is solely imposed by an abrupt shift in sampling rate and uncovers periods of reduced seasonal rainfall predictability associated with enhanced El Nino-Southern Oscillation and tropical cyclone activity.Article Citation - WoS: 4Citation - Scopus: 7Collective dynamics of random Janus oscillator networks(AMER PHYSICAL SOC, 2020) Peron, Thomas; Eroğlu, Deniz; Rodrigues, Francisco A.; Moreno, YamirJanus 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.Doctoral Thesis Network Reconstruction From Data(Kadir Has Üniversitesi, 2023) Kement, İrem Topal; Eroğlu, DenizGüç 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.Master Thesis Generalized Synchronization: Master-Slave Relationship in Three Coupled Systems(Kadir Has Üniversitesi, 2022) Doğan, Gizem; Eroglu, DenizSynchronization 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.Article Citation - WoS: 27Citation - Scopus: 26Emergent hypernetworks in weakly coupled oscillators(Nature Portfolio, 2022) Nijholt, Eddie; Ocampo-Espindola, Jorge Luis; Eroglu, Deniz; Kiss, Istvan Z.; Pereira, TiagoNetworks 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.Article Forecasting Critical Economic & Political Events Via Electricity Consumption Patterns in the United States of America and Turkey(Springernature, 2025) Ozdes, Celik; Ediger, Volkan S.; Eroglu, DenizImpacts from natural disasters, government decisions and public's reactions can significantly alter societal daily routines. These effects resonate in systems where individual contributions, such as energy consumption, serve as indirect indicators of societal welfare and living standards. Preparedness for unforeseen events is crucial to enhancing societal well-being. Thus, analysing historical data for unexpected critical transitions and forecasting future occurrences is paramount. Recurrence properties of gross monthly electricity consumption in the United States of America and Turkey are examined, revealing coinciding critical periods with extreme regimes identified by a determinism time series. An ensemble of neural network proxies is then employed to forecast critical periods within a limited time frame, enabling the anticipation of similar occurrences. Validation of this approach demonstrates high predictive performance when measured quantities adequately reflect underlying system dynamics. Predictions based on electricity consumption data suggest potential systemic and socioeconomic crises for both nations within one year, with probabilities, 85% for the US and 32% for Turkey.Master Thesis The Effect of Link Modifications on Network Synchronization(Kadir Has Üniversitesi, 2022) KIRAN, NARÇİÇEĞİ; Eroglu, DenizA 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.Article Fourier's Law Breakdown for the Planar-Rotor Chain with Long-Range Interactions(Elsevier, 2026) Lima, Henrique Santos; Tsallis, Constantino; Eroglu, Deniz; Tirnakli, UgurFourier's law, which linearly relates heat flux to the negative gradient of temperature, is a fundamental principle in thermal physics and widely applied across materials science and engineering. However, its validity in low-dimensional systems with long-range interactions remains only partially understood. We investigate here the thermal transport along a onedimensional chain of classical planar rotators with algebraically decaying interactions 1/ with distance ( >= 0), known as the inertial a-XY model. Using nonequilibrium simulations with thermal reservoirs at the boundaries, we numerically study the thermal conductance as a function of system sizea, temperature , and . We find that the results obey a universal scaling law characterized by a stretched-exponential function with -dependent parameters. Notably, a threshold at approximate to 2 separates two regimes: for >= , Fourier's law holds with size-independent conductivity = , while for < , anomalous transport is observed, corroborating (with higher precision) the results reported in Phys.Rev.E94,042117(2016). These findings provide a quantitative framework for understanding the breakdown of Fourier's law in systems with long-range interactions. The simulation is carried out by assuming the equations of motion, which include Langevin heat baths applied to the first and last particles, and are integrated using the Velocity Verlet algorithm. The conductance is calculated from the connection between Lagrangian heat flux and heat equation for typical values of (, , ). For large , the results can be collapsed into an universal -stretched exponential form, namely proportional to -() , where = [1 + (1-)]1/(1-). The parameters (, , ,) are -dependent, and is the index of the -stretched exponential. This form is achievable due to the ratio /( - 1) being almost constant with respect to the lattice size. These findings provide significant insights into heat conduction mechanisms in systems with long-range interactions.Article Citation - Scopus: 7Collective 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.
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