arXiv subjects

Xiaozhu Zhang

Publications and source records attributed to Xiaozhu Zhang.

16 recordsLinked to original sources

arXiv2026

Low-Dimensional Phase Diagram of Higher-Order Networked Systems

Higher-order networks exhibit rich critical phenomena that cannot be captured by traditional pairwise models. Here, we develop an analytical dimension-reduction framework that maps higher-order networked dynamics onto an effective low-dimensional system, allowing accurate prediction of tipping boundaries, bistability regions, and the nature of phase transitions. We demonstrate the power of this framework across a range of dynamical processes, revealing distinct effects of higher-order interactions on transition continuity and hysteresis. Furthermore, we find that system resilience exhibits a profound dependence on the alignment between pairwise and higher-order connectivity, with assortative mixing enhancing tipping toward active states. Our findings establish a general theory for understanding the critical transitions in higher-order networks, offering new insights for anticipating and managing systemic risk in complex systems.

physics.soc-ph↗

arXiv2026

Quantifying uncertainty and stability among highly correlated predictors: a subspace perspective

We study the problem of linear feature selection when features are highly correlated. Such settings pose two fundamental challenges. First, how should model similarity be defined? Simply counting features in common can be misleading: two models may share no features, yet highly correlated features can make the two models very similar in terms of predictive ability. Second, how can feature stability be assessed across runs of a variable selection method? High correlation can yield very different feature sets, so counting how often a feature is selected may label most features as unstable, and selecting stable features would result in models that are too small with poor predictive performance. In essence, these issues arise because existing notions of similarity and stability are "discrete" in nature. To overcome these challenges, we propose a novel framework based on feature subspaces -- the subspaces spanned by selected columns of the feature matrix. This new perspective leads to "continuous" measures of similarity and stability, as well as false positive error, all of which are defined in terms of "closeness" of feature subspaces. Our measures naturally account for feature correlation and reduce to existing discrete notions when features are uncorrelated. To obtain stable models, we propose and theoretically analyze a subspace-based generalization of stability selection (Meinshausen & Bühlmann 2010, Taeb et al. 2020), which combines a discrete model search with a continuous subspace-based assessment of stability. On synthetic and real gene expression data, our method improves on existing stability-based approaches by (i) producing multiple stable models that capture feature interchangeability, and (ii) generating larger models with better predictive performance. Our method is implemented in the R package substab.

stat.ME↗

arXiv2025

Convex Mixed-Integer Programming for Causal Additive Models with Optimization and Statistical Guarantees

We study the problem of learning a directed acyclic graph from data generated according to an additive, non-linear structural equation model with Gaussian noise. We express each non-linear function through a basis expansion, and derive a maximum likelihood estimator with a group l0-regularization that penalizes the number of edges in the graph. The resulting estimator is formulated through a convex mixed-integer program, enabling the use of branch-and-bound methods to obtain a solution that is guaranteed to be accurate up to a pre-specified optimality gap. Our formulation can naturally encode background knowledge, such as the presence or absence of edges and partial order constraints among the variables. We establish consistency guarantees for our estimator in terms of graph recovery, even when the number of variables grows with the sample size. Additionally, by connecting the optimality guarantees with our statistical error bounds, we derive an early stopping criterion that allows terminating the branch-and-bound procedure while preserving consistency. Compared with existing approaches that either assume equal error variances, restrict to linear structural equation models, or rely on heuristic procedures, our method enjoys both optimization and statistical guarantees. Extensive simulations and real-data analysis show that the proposed method achieves markedly better graph recovery performance.

stat.ME↗

arXiv2024

Fluctuation Response Patterns of Network Dynamics -- an Introduction

Networked dynamical systems, i.e., systems of dynamical units coupled via nontrivial interaction topologies, constitute models of broad classes of complex systems, ranging from gene regulatory and metabolic circuits in our cells to pandemics spreading across continents. Most of such systems are driven by irregular and distributed fluctuating input signals from the environment. Yet how networked dynamical systems collectively respond to such fluctuations depends on the location and type of driving signal, the interaction topology and several other factors and remains largely unknown to date. As a key example, modern electric power grids are undergoing a rapid and systematic transformation towards more sustainable systems, signified by high penetrations of renewable energy sources. These in turn introduce significant fluctuations in power input and thereby pose immediate challenges to the stable operation of power grid systems. How power grid systems dynamically respond to fluctuating power feed-in as well as other temporal changes is critical for ensuring a reliable operation of power grids yet not well understood. In this work, we systematically introduce a linear response theory for fluctuation-driven networked dynamical systems. The derivations presented not only provide approximate analytical descriptions of the dynamical responses of networks, but more importantly, allows to extract key qualitative features about spatio-temporally distributed response patterns. Specifically, we provide a general formulation of a linear response theory for perturbed networked dynamical systems, explicate how dynamic network response patterns arise from the solution of the linearized response dynamics, and emphasize the role of linear response theory in predicting and comprehending power grid responses on different temporal and spatial scales and to various types of disturbances.

nlin.AO↗

arXiv2024

Topological Determinants of Perturbation Spreading in Networks

Spreading phenomena essentially underlie the dynamics of various natural and technological networked systems, yet how spatiotemporal propagation patterns emerge from such networks remains largely unknown. Here we propose a novel approach that reveals universal features determining the spreading dynamics in diffusively coupled networks and disentangles them from factors that are system specific. In particular, we first analytically identify a purely topological factor encoding the interaction structure and strength, and second, numerically estimate a master function characterizing the universal scaling of the perturbation arrival times across topologically different networks. The proposed approach thereby provides intuitive insights into complex propagation patterns as well as accurate predictions for the perturbation arrival times. The approach readily generalizes to a wide range of networked systems with diffusive couplings and may contribute to assess the risks of transient influences of ubiquitous perturbations in real-world systems.

physics.soc-ph↗

arXiv2022

A Generator for Generalized Inverse Gaussian Distributions

We propose a new generator for the generalized inverse Gaussian (GIG) distribution by decomposing the density of GIG into two components. The first component is a truncated inverse Gamma density, in order to sample from which we improve the traditional inverse CDF method. The second component is the product of an exponential pdf and an inverse Gamma CDF. In order to sample from this quasi-density, we develop a rejection sampling procedure that adaptively adjusts the piecewise proposal density according to the user-specified rejection rate or the desired number of cutoff points. The resulting complete algorithm enjoys controllable rejection rate and moderate setup time. It preserves efficiency for both parameter varying case and large sample case.

stat.CO↗

arXiv2021

Absence of pure voltage instabilities in the third order model of power grid dynamics

Secure operation of electric power grids fundamentally relies on their dynamical stability properties. For the third order model, a paradigmatic model that captures voltage dynamics, three routes to instability are established in the literature, a pure rotor angle instability, a pure voltage instability and one instability induced by the interplay of both. Here we demonstrate that one of these routes, the pure voltage instability, is inconsistent with Kirchhoff's nodal law and thus nonphysical. We show that voltage collapse dynamics nervertheless exist in the absence of any voltage instability.

physics.app-ph↗

arXiv2021

Dynamic perturbation spreading in networks

Understanding how local perturbations induce the transient dynamics of a network of coupled units is essential to control and operate such systems. Often a perturbation initiated in one unit spreads to other units whose dynamical state they transiently alter. The maximum state changes at those units and the timings of these changes constitute key characteristics of such transient response dynamics. However, even for linear dynamical systems it is not possible to analytically determine time and amplitude of the maximal response of a unit to a perturbation. Here, we propose to extract approximate peak times and amplitudes from effective expectation values used to characterize the typical time and magnitude of the response of a unit by interpreting the system's response as a probability distribution over time. We derive analytic estimators for the peak response based on these expectation value measures in linearized systems operating close to a stable fixed point. These estimators can be expressed in terms of the inverse of the system's Jacobian. We obtain identical results with different approximations for the response dynamics, indicating that these estimators become exact in the limit of weak coupling. Furthermore, the results suggest that perturbations spread ballistically in networks with diffusive coupling.

physics.soc-ph↗

arXiv2019

Dynamic Vulnerability in Oscillatory Networks and Power Grids

Recent work found distributed resonances in driven oscillator networks and AC power grids. The emerging dynamic resonance patterns are highly heterogeneous and nontrivial, depending jointly on the driving frequency, the interaction topology of the network and the node or nodes driven. Identifying which nodes are most susceptible to dynamic driving and may thus make the system as a whole vulnerable to external input signals, however, remains a challenge. Here we propose an easy-to-compute Dynamic Vulnerability Index (DVI) for identifying those nodes that exhibit largest amplitude responses to dynamic driving signals with given power spectra and thus are most vulnerable. The DVI is based on linear response theory, as such generic, and enables robust predictions. It thus shows potential for a wide range of applications across dynamically driven networks, for instance for identifying the vulnerable nodes in power grids driven by fluctuating inputs from renewable energy sources and fluctuating power output to households.

nlin.AO↗

arXiv2018

Fluctuation-induced Distributed Resonances in Oscillatory Networks

Self-organized network dynamics prevails for systems across physics, biology and engineering. How external signals generate distributed responses in networked systems fundamentally underlies their function, yet is far from fully understood. Here we analyze the dynamic response patterns of oscillatory networks to fluctuating input signals. We disentangle the impact of the signal distribution across the network, the signals' frequency contents and the network topology. We analytically derive qualitatively different dynamic response patterns and find three frequency regimes: homogeneous responses at low frequencies, topology-dependent resonances at intermediate frequencies, and localized responses at high frequencies. The theory faithfully predicts the network-wide collective responses to regular and irregular, localized and distributed simulated signals, as well as to real input signals to power grids recorded from renewable-energy supplies. These results not only provide general insights into the formation of dynamic response patterns in networked systems but also suggest regime- and topology-specific design principles underlying network function.

nlin.AO↗

arXiv2017

Quantifying Transient Spreading Dynamics on Networks

Spreading phenomena on networks are essential for the collective dynamics of various natural and technological systems, from information spreading in gene regulatory networks to neural circuits or from epidemics to supply networks experiencing perturbations. Still, how local disturbances spread across networks is not yet quantitatively understood. Here we analyze generic spreading dynamics in deterministic network dynamical systems close to a given operating point. Standard dynamical systems' theory does not explicitly provide measures for arrival times and amplitudes of a transient, spreading signal because it focuses on invariant sets, invariant measures and other quantities less relevant for transient behavior. We here change the perspective and introduce effective expectation values for deterministic dynamics to work out a theory explicitly quantifying when and how strongly a perturbation initiated at one unit of a network impacts any other. The theory provides explicit timing and amplitude information as a function of the relative position of initially perturbed and responding unit as well as on the entire network topology.

physics.soc-ph↗

arXiv2017

Escape Routes, Weak Links, and Desynchronization in Fluctuation-driven Networks

Shifting our electricity generation from fossil fuel to renewable energy sources introduces large fluctuations to the power system. Here, we demonstrate how increased fluctuations, reduced damping and reduced intertia may undermine the dynamical robustness of power grid networks. Focusing on fundamental noise models, we derive analytic insights into which factors limit the dynamic robustness and how fluctuations may induce a system escape from an operating state. Moreover, we identify weak links in the grid that make it particularly vulnerable to fluctuations. These results thereby not only contribute to a theoretical understanding of how fluctuations act on distributed network dynamics, they may also help designing future renewable energy systems to be more robust.

nlin.AO↗

arXiv2016

Network susceptibilities: theory and applications

We introduce the concept of network susceptibilities quantifying the response of the collective dy- namics of a network to small parameter changes. We distinguish two types of susceptibilities: vertex susceptibilities and edge susceptibilities, measuring the responses due to changes in the properties of units and their interactions, respectively. We derive explicit forms of network susceptibilities for oscillator networks close to steady states and offer example applications for Kuramoto-type phase- oscillator models, power grid models and generic flow models. Focusing on the role of the network topology implies that these ideas can be easily generalized to other types of networks, in particular those characterizing flow, transport, or spreading phenomena. The concept of network susceptibil- ities is broadly applicable and may straightforwardly be transferred to all settings where networks responses of the collective dynamics to topological changes are essential.

nlin.AO↗

arXiv2015

Predictability of Critical Transitions

Critical transitions in multistable systems have been discussed as models for a variety of phenomena ranging from the extinctions of species to socio-economic changes and climate transitions between ice-ages and warm-ages. From bifurcation theory we can expect certain critical transitions to be preceded by a decreased recovery from external perturbations. The consequences of this critical slowing down have been observed as an increase in variance and autocorrelation prior to the transition. However especially in the presence of noise it is not clear, whether these changes in observation variables are statistically relevant such that they could be used as indicators for critical transitions. In this contribution we investigate the predictability of critical transitions in conceptual models. We study the quadratic integrate-and-fire model and the van der Pol model, under the influence of external noise. We focus especially on the statistical analysis of the success of predictions and the overall predictability of the system. The performance of different indicator variables turns out to be dependent on the specific model under study and the conditions of accessing it. Furthermore, we study the influence of the magnitude of transitions on the predictive performance.

physics.data-an↗

arXiv2015

Critical links and nonlocal rerouting in complex supply networks

Link failures repeatedly induce large-scale outages in power grids and other supply networks. Yet, it is still not well understood, which links are particularly prone to inducing such outages. Here we analyze how the nature and location of each link impact the network's capability to maintain stable supply. We propose two criteria to identify critical links on the basis of the topology and the load distribution of the network prior to link failure. They are determined via a link's redundant capacity and a renormalized linear response theory we derive. These criteria outperform critical link prediction based on local measures such as loads. The results not only further our understanding of the physics of supply networks in general. As both criteria are available before any outage from the state of normal operation, they may also help real-time monitoring of grid operation, employing counter-measures and support network planning and design.

physics.soc-ph↗

arXiv2012

Performance of Polarization-based Stereoscopy Screens

The screen is a key part of stereoscopic display systems using polarization to separate the different channels for each eye. The system crosstalk, characterizing the imperfection of the screen in terms of preserving the polarization of the incoming signal, and the scattering rate, characterizing the ability of the screen to deliver the incoming light to the viewers, determine the image quality of the system. Both values will depend on the viewing angle. In this work we measure the performance of three silver screens and three rear-projection screens. Additionally, we measure the surface texture of the screens using white-light interferometry. While part of our optical results can be explained by the surface roughness, more work is needed to understand the optical properties of the screens from a microscopic model.

physics.optics↗