arXiv subjects

Cook Hyun Kim

Publications and source records attributed to Cook Hyun Kim.

14 recordsLinked to original sources

arXiv2026

Synchronization Pathways and Resilience in Power Grids

Ensuring a sustainable energy supply requires maintaining power-grid stability. Rotor dynamics are governed by the swing equation, which takes the form of a second-order Kuramoto model with a correlation between power and total coupling strength. Yet the microscopic mechanisms that nucleate and propagate synchronized clusters remain poorly understood. Using a minimal model motivated by empirical grid data and the \Adhoc potential method, we reveal two distinct seed cluster types and propagation pathways: a population-driven seed cluster at the center of the power distribution propagating to its tails by rotor accretion, and, for a symmetric distribution, coupling-driven seed clusters at its tails propagating inward to the center by cluster merger. These differences generate distinct order-parameter patterns, while inertia controls whether the seed clusters persist with distinct angular velocities. The same propagation pathways also govern recovery following external disturbances. We further confirm that the same selection--persistence rule holds in data-derived annealed representations of European power grids. Therefore, our results can inform strategies for sustaining stable power-grid operation. More generally, our pathway-based framework reframes synchronization by emphasizing the dynamics of cluster formation and recovery rather than relying solely on static criteria.

nlin.AO↗

arXiv2026

Universal scaling and protocol-dependent amplitude in hybrid quantum walks

Quantum walks spread ballistically but become diffusive with classical admixture. We compare two hybrid walks using identical quantum and classical steps at the same classical-step rate but ordered differently in time. Any nonzero admixture yields diffusion. Near the quantum limit, both diffusion coefficients have first-order poles, but their amplitudes differ by about a factor of two. The coherence timescale sets the pole order, while temporal organization sets the amplitude. We expect our analytical framework to apply broadly to quantum--classical hybrid systems.

quant-ph↗

arXiv2026

Paths to synchronization in the Kuramoto model with inertia

Synchronization is ubiquitous across natural and synthetic systems, yet most prior studies focus on the inertia-free Kuramoto model and do so at the macroscopic level. In this study, we instead investigate the inertial Kuramoto model and analyze the kinetics of individual synchronized clusters that emerge in the underdamped dynamics, driven by the interactions among multiple synchronized clusters with different frequencies. Specifically, we explore two forms of intrinsic frequency distribution -- unimodal Gaussian and multimodal uniform -- and show that they give rise to qualitatively different synchronized clusters: a hierarchical organization for the Gaussian distribution and a homogeneous organization for the uniform distribution. This contrast leads to qualitatively different behaviors of the order parameter: for the Gaussian distribution, it increases smoothly with increasing coupling strength, while for the uniform distribution, it grows through a series of discrete jumps that trace out the size of the Devil's staircase (DS). By resolving the kinetics at the cluster level, we further find that the route to synchronization also depends on the distribution type: with a Gaussian distribution, a single dominant cluster forms and gradually entrains the remaining oscillators, whereas with a uniform distribution, synchronization proceeds via successive cluster mergers initiated from peripheral seeds associated with the high-frequency periphery. Taken together, these findings provide a new perspective on collective synchronization dynamics in inertial complex systems.

nlin.AO↗

arXiv2026

Heterogeneous Network Topology Induces the Widom Line

The Widom line, initially identified as a crossover line between liquid-like and gas-like behavior in water and supercritical fluids, separates these two types of behavior. Here, we show that an analogous line arises in spin models on scale-free networks as a consequence of degree heterogeneity, which we analyze using the annealed network approximation. For the Ashkin--Teller and Invisible Potts models, the Widom line exists within a finite range of the degree exponent. It separates two distinct ordered regimes$-$distributed spin alignment and hub-dominant alignment$-$while also giving rise to a supercritical-like state where the two alignments become indistinguishable. These results demonstrate that degree heterogeneity alone can generate mesoscopic crossovers beyond conventional phase-transition theory, opening new directions for understanding and controlling collective dynamics in complex networks.

physics.soc-ph↗

arXiv2026

Phase lag enhances synchronization in coupled oscillators with inertia

The second-order Kuramoto model with inertia exhibits different dynamical behaviors than the first-order KM without inertia. A central difference is its lower synchronization due to the emergence of multiple synchronized clusters with different frequencies. We aim to investigate how such lowered synchronization can be improved by applying external perturbations to the system in a steady state, for example, a symmetry-breaking phase lag to a subset of oscillators. We find that this phase lag steers the primary cluster along a specific path and enables it to merge with higher-order clusters, thereby enhancing global synchronization. Our results reveal a mechanism by which controlled phase lag can improve entrainment in inertial oscillator systems, with possible implications for synchronization control in inertial oscillator networks.

cond-mat.stat-mech↗

arXiv2025

Cluster-Mediated Synchronization Dynamics in Globally Coupled Oscillators with Inertia

Globally coupled oscillator systems with inertia exhibit complex synchronization patterns, among which the emergence of a couple of secondary synchronized clusters (SCs) in addition to the primary cluster (PC) is especially distinctive. Although previous studies have predominantly focused on the collective properties of the PC, the dynamics of individual clusters and their inter-cluster interactions remain largely unexplored. Here, we demonstrate that multiple clusters emerge and coexist, forming a hierarchical pattern known as the Devil's Staircase. We identify three key findings by investigating individual cluster dynamics and inter-cluster interactions. First, the PC persistently suppresses the formation of SCs during its growth and even after it has fully formed, revealing the significant impact of inter-cluster interactions on cluster formation. Second, once established, SCs induce higher-order clusters exhibiting frequency resonance via inter-cluster interactions, resulting in the Devil's Staircase pattern. Third, sufficiently large SCs can destabilize and fragment the PC, highlighting the bidirectional nature of cluster interactions. We develop a coarse-grained Kuramoto model that treats each cluster as a macroscopic oscillator to capture these inter-cluster dynamics and the resulting phenomena. Our work marks a significant step beyond system-wide averages in the study of inertial oscillator systems, offering new insights into the rich dynamics of cluster formation and synchronization in real-world applications such as power grid networks.

nlin.AO↗

arXiv2025

Ashkin-Teller model with antiferromagnetic four-spin interactions: Interference effect between two conflicting issues

Spin systems have emerged as powerful tools for understanding collective phenomena in complex systems. In this work, we investigate the Ashkin--Teller (AT) model on random scale-free networks using mean-field theory, which extends the traditional Ising framework by coupling two spin systems via both pairwise and four-spin interactions. We focus on the previously unexplored antiferromagnetic regime of four-spin coupling, in which strong ordering in one layer actively suppresses the formation of order in the other layer. This mechanism captures, for example, scenarios in social or political systems where a dominant viewpoint on one issue (e.g., economic development) can inhibit consensus on another (e.g., environmental conservation). Our analysis reveals a rich phase diagram with four distinct phases -- paramagnetic, Baxter, \langle \sigma \rangle, and antiferromagnetic -- and diverse types of phase transitions. Notably, we find that the upper critical degree exponent extends to \lambda_{c2} \approx 9.237, far exceeding the conventional value of \lambda = 5$ observed in ferromagnetic systems. This dramatic shift underscores the enhanced robustness of hub-mediated spin correlations under competitive coupling, leading to asymmetric order parameters between layers and novel phase transition phenomena. These findings offer fundamental insights into systems with competing order parameters and have direct implications for multilayer biological networks, social media ecosystems, and political debates characterized by competing priorities.

physics.soc-ph↗

arXiv2025

From Spatial to Spectral: Network Renormalization via Dynamical Correlations

Network renormalization has traditionally relied on spatial adjacency-grouping nearby nodes together, but this approach fails to capture the dynamical correlations that govern system-wide behavior in scale-free networks. We present a spectral-space renormalization framework that enables coarse-graining based on dynamical coherence rather than geometric proximity. Within this framework, diffusion processes naturally constitute renormalization transformations in spectral space, yielding scaling relations that connect network dimensions with critical exponents. Building on this foundation, we develop a meta-graph reconstruction algorithm that systematically maps spectral information back into explicit topology while preserving dynamical correlations. The resulting renormalized networks uncover organizational structures that remain invisible to adjacency-based methods, including long-range correlations between structurally distant nodes that reflect coherent dynamical responses. Applications to Internet topologies, yeast regulatory networks, and European power grids demonstrate the broad applicability of this framework. The algorithm consistently extracts fractal, spectral, and random-walk dimensions with theoretical consistency across diverse systems. In power grids, it further reveals hidden failure pathways, exposing transcontinental correlations that match documented cascade patterns. In Internet networks, it reveals multiscaling behavior as the topology evolves over time. By shifting network renormalization from spatial geometry to dynamical flow, this work provides a unified foundation for understanding how information, energy, and failures propagate through complex systems, with direct implications for infrastructure resilience and network vulnerability assessment.

physics.soc-ph↗

arXiv2025

Optimal location of reinforced inertia to stabilize power grids

The increasing adoption of renewable energy sources has significantly reduced the inertia in the modernized power grid, making the system more vulnerable. One way to stabilize the grid is to add extra inertia from unused turbines, called the fast frequency response (FFR), to the existing grid. However, reinforcing inertia can cause unintended consequences, such as more significant avalanche failures. This phenomenon is known as the Braess paradox. Here, we propose a method to find the optimal position of FFR. This method is applied to the second-order Kuramoto model to find an effective position to mitigate cascading failures. To address this, we propose a method to evaluate a ratio between the positive effects of mitigation and the negative consequences. Through this analysis, we find that the peripheral area of the network is a seemingly effective location for inertia reinforcement across various reinforcement scales. This strategy provides essential insights for enhancing the stability of power grids in a time of widespread renewable energy usage.

physics.soc-ph↗

arXiv2024

Reinforcement Learning Optimizes Power Dispatch in Decentralized Power Grid

Effective frequency control in power grids has become increasingly important with the increasing demand for renewable energy sources. Here, we propose a novel strategy for resolving this challenge using graph convolutional proximal policy optimization (GC-PPO). The GC-PPO method can optimally determine how much power individual buses dispatch to reduce frequency fluctuations across a power grid. We demonstrate its efficacy in controlling disturbances by applying the GC-PPO to the power grid of the UK. The performance of GC-PPO is outstanding compared to the classical methods. This result highlights the promising role of GC-PPO in enhancing the stability and reliability of power systems by switching lines or decentralizing grid topology.

physics.soc-ph↗

arXiv2024

Entropy-Induced Phase Transitions in a Hidden Potts Model

A hidden state in which a spin does not interact with any other spin contributes to the entropy of an interacting spin system. Using the Ginzburg-Landau formalism in the mean-field limit, we explore the $q$-state Potts model with extra $r$ hidden states. We analytically demonstrate that when $1 < q \le 2$, the model exhibits a rich phase diagram comprising a variety of phase transitions such as continuous, discontinuous, two types of hybrids, and two consecutive second- and first-order transitions; moreover, several characteristics such as critical, critical endpoint, and tricritical point are identified. The critical line and critical end lines merge in a singular form at the tricritical point. Those complex critical behaviors are not wholly detected in previous research because the research is implemented only numerically. We microscopically investigate the origin of the discontinuous transition; it is induced by the competition between the interaction and entropy of the system in the Ising limit, whereas by the bi-stability of the hidden spin states in the percolation limit. Finally, we discuss the potential applications of the hidden Potts model to social opinion formation with shy voters and the percolation in interdependent networks.

cond-mat.stat-mech↗

arXiv2022

Prediction and mitigation of nonlocal cascading failures using graph neural networks

Cascading failures (CFs) in electrical power grids propagate nonlocally; After a local disturbance, the second failure may be distant. To study the avalanche dynamics and mitigation strategy of nonlocal CFs, numerical simulation is necessary; however, computational complexity is high. Here, we first propose an avalanche centrality (AC) of each node, a measure related to avalanche size, based on the Motter and Lai model. Second, we train a graph neural network (GNN) with the AC in small networks. Next, the trained GNN predicts the AC ranking in much larger networks and real-world electrical grids. This result can be used effectively for avalanche mitigation. The framework we develop can be implemented in other complex processes that are computationally costly to simulate in large networks.

physics.soc-ph↗

arXiv2021

Link overlap influences opinion dynamics on multiplex networks: spin model approach

Consider a multiplex network formed by two layers indicating social interactions: the first layer is a friendship network and the second layer is a network of business relations. In this duplex network each pair of individuals can be connected in different ways: they can be connected by a friendship but not connected by a business relation, they can be connected by a business relation without being friends, or they can be simultaneously friends and in a business relation. In the latter case we say that the links in different layers overlap. These three types of connections are called multilinks and the multidegree indicates the sum of multilinks of a given type that are incident to a given node. Previous models of opinion formation on multilayer networks have mostly neglected the effect of link overlap. Here we show that link overlap can have important effects in opinion formation. Indeed, emerging pattern of opinion formation can be significantly influenced by the statistical properties of multilinks, and in particular by the multidegree distribution. To quantitatively address this problem, we study a simple spin model, called the Ashkin-Teller model including 2-body and 4-body interactions between nodes in different layers. Here we fully investigate the rich phase diagram of this model which includes a large variety of phase transitions (PTs). Indeed the phase digram or the model displays continuous, discontinuous, successive discontinuous, and hybrid PTs.

physics.soc-ph↗

arXiv2015

Stochastic Dynamics of Infrared Fluctuations in Accelerating Universe

We extend investigations of infrared dynamics in accelerating universes. In the presence of massless and minimally coupled scalar fields, physical quantities may acquire growing time dependences through quantum fluctuations at super-horizon scales. From a semiclassical viewpoint, it was proposed that such infrared effects are described by a Langevin equation. In de Sitter space, the stochastic approach has been proved to be equivalent to resummation of the growing time dependences at the leading power. In this paper, we make the resummation derivation of the Langevin equation in a general accelerating universe. We first consider an accelerating universe whose slow-roll parameter is constant, and then extend the background as the slow-roll parameter becomes time dependent. The resulting Langevin equation contains a white noise term and the coefficient of each term is modified by the slow-roll parameter. Furthermore we find that the semiclassical description of the scalar fields leads to the same stochastic equation as far as we adopt an appropriate time coordinate.

hep-th↗