ASU Scholarship Showcase

In spite of the recent interest and advances in linear controllability of complex networks, controlling nonlinear network dynamics remains an outstanding problem. Here we develop an experimentally feasible control framework for nonlinear dynamical networks that exhibit multistability. The control objective is to apply parameter perturbation to drive the system from one attractor to another, assuming that the former is undesired and the latter is desired. To make our framework practically meaningful, we consider restricted parameter perturbation by imposing two constraints: it must be experimentally realizable and applied only temporarily. We introduce the concept of attractor network, which allows us to formulate a quantifiable controllability framework for nonlinear dynamical networks: a network is more controllable if the attractor network is more strongly connected. We test our control framework using examples from various models of experimental gene regulatory networks and demonstrate the beneficial role of noise in facilitating control.

Our ability to uncover complex network structure and dynamics from data is fundamental to understanding and controlling collective dynamics in complex systems. Despite recent progress in this area, reconstructing networks with stochastic dynamical processes from limited time series remains to be an outstanding problem. Here we develop a framework based on compressed sensing to reconstruct complex networks on which stochastic spreading dynamics take place. We apply the methodology to a large number of model and real networks, finding that a full reconstruction of inhomogeneous interactions can be achieved from small amounts of polarized (binary) data, a virtue of compressed sensing. Further, we demonstrate that a hidden source that triggers the spreading process but is externally inaccessible can be ascertained and located with high confidence in the absence of direct routes of propagation from it. Our approach thus establishes a paradigm for tracing and controlling epidemic invasion and information diffusion in complex networked systems.

Urban environmental measurements and observational statistics should reflect the properties generated over an adjacent area of adequate length where homogeneity is usually assumed. The determination of this characteristic source area that gives sufficient representation of the horizontal coverage of a sensing instrument or the fetch of transported quantities is of critical importance to guide the design and implementation of urban landscape planning strategies. In this study, we aim to unify two different methods for estimating source areas, viz. the statistical correlation method commonly used by geographers for landscape fragmentation and the mechanistic footprint model by meteorologists for atmospheric measurements. Good agreement was found in the intercomparison of the estimate of source areas by the two methods, based on 2-m air temperature measurement collected using a network of weather stations. The results can be extended to shed new lights on urban planning strategies, such as the use of urban vegetation for heat mitigation. In general, a sizable patch of landscape is required in order to play an effective role in regulating the local environment, proportional to the height at which stakeholders’ interest is mainly concerned.

Nocturnal cooling of urban areas governs the evolution of thermal state and many thermal-driven environmental issues in cities, especially those suffer strong urban heat island (UHI) effect. Advances in the fundamental understanding of the underlying physics of nighttime UHI involve disentangling complex contributing effects and remains an open challenge. In this study, we develop new numerical algorithms to characterize the thermodynamics of urban nocturnal cooling based on solving the energy balance equations for both the landscape surface and the overlying atmosphere. Further, a scaling law is proposed to relate the UHI intensity to a range of governing mechanisms, including the vertical and horizontal transport of heat in the surface layer, the urban-rural breeze, and the possible urban expansion. The accuracy of proposed methods is evaluated against in-situ urban measurements collected in cities with different geographic and climatic conditions. It is found that the vertical and horizontal contributors modulate the nocturnal UHI at distinct elevation in the atmospheric boundary layer.

Evolutionary games model a common type of interactions in a variety of complex, networked, natural systems and social systems. Given such a system, uncovering the interacting structure of the underlying network is key to understanding its collective dynamics. Based on compressive sensing, we develop an efficient approach to reconstructing complex networks under game-based interactions from small amounts of data. The method is validated by using a variety of model networks and by conducting an actual experiment to reconstruct a social network. While most existing methods in this area assume oscillator networks that generate continuous-time data, our work successfully demonstrates that the extremely challenging problem of reverse engineering of complex networks can also be addressed even when the underlying dynamical processes are governed by realistic, evolutionary-game type of interactions in discrete time.

Urban land–atmosphere interactions can be captured by numerical modeling framework with coupled land surface and atmospheric processes, while the model performance depends largely on accurate input parameters. In this study, we use an advanced stochastic approach to quantify parameter uncertainty and model sensitivity of a coupled numerical framework for urban land–atmosphere interactions. It is found that the development of urban boundary layer is highly sensitive to surface characteristics of built terrains. Changes of both urban land use and geometry impose significant impact on the overlying urban boundary layer dynamics through modification on bottom boundary conditions, i.e., by altering surface energy partitioning and surface aerodynamic resistance, respectively. Hydrothermal properties of conventional and green roofs have different impacts on atmospheric dynamics due to different surface energy partitioning mechanisms. Urban geometry (represented by the canyon aspect ratio), however, has a significant nonlinear impact on boundary layer structure and temperature. Besides, managing rooftop roughness provides an alternative option to change the boundary layer thermal state through modification of the vertical turbulent transport. The sensitivity analysis deepens our insight into the fundamental physics of urban land–atmosphere interactions and provides useful guidance for urban planning under challenges of changing climate and continuous global urbanization.

Locating sources of diffusion and spreading from minimum data is a significant problem in network science with great applied values to the society. However, a general theoretical framework dealing with optimal source localization is lacking. Combining the controllability theory for complex networks and compressive sensing, we develop a framework with high efficiency and robustness for optimal source localization in arbitrary weighted networks with arbitrary distribution of sources. We offer a minimum output analysis to quantify the source locatability through a minimal number of messenger nodes that produce sufficient measurement for fully locating the sources. When the minimum messenger nodes are discerned, the problem of optimal source localization becomes one of sparse signal reconstruction, which can be solved using compressive sensing. Application of our framework to model and empirical networks demonstrates that sources in homogeneous and denser networks are more readily to be located. A surprising finding is that, for a connected undirected network with random link weights and weak noise, a single messenger node is sufficient for locating any number of sources. The framework deepens our understanding of the network source localization problem and offers efficient tools with broad applications.

Dynamical processes occurring on the edges in complex networks are relevant to a variety of real-world situations. Despite recent advances, a framework for edge controllability is still required for complex networks of arbitrary structure and interaction strength. Generalizing a previously introduced class of processes for edge dynamics, the switchboard dynamics, and exploit- ing the exact controllability theory, we develop a universal framework in which the controllability of any node is exclusively determined by its local weighted structure. This framework enables us to identify a unique set of critical nodes for control, to derive analytic formulas and articulate efficient algorithms to determine the exact upper and lower controllability bounds, and to evaluate strongly structural controllability of any given network. Applying our framework to a large number of model and real-world networks, we find that the interaction strength plays a more significant role in edge controllability than the network structure does, due to a vast range between the bounds determined mainly by the interaction strength. Moreover, transcriptional regulatory networks and electronic circuits are much more strongly structurally controllable (SSC) than other types of real-world networks, directed networks are more SSC than undirected networks, and sparse networks are typically more SSC than dense networks.

The net storage heat flux (ΔQ[subscript S]) is important in the urban surface energy balance (SEB) but its determination remains a significant challenge. The hysteresis pattern of the diurnal relation between the ΔQ[subscript S] and net all-wave radiation (Q[superscript ∗]) has been captured in the Objective Hysteresis Model (OHM) parameterization of ΔQ[subscript S]. Although successfully used in urban areas, the limited availability of coefficients for OHM hampers its application. To facilitate use, and enhance physical interpretations of the OHM coefficients, an analytical solution of the one-dimensional advection–diffusion equation of coupled heat and liquid water transport in conjunction with the SEB is conducted, allowing development of AnOHM (Analytical Objective Hysteresis Model). A sensitivity test of AnOHM to surface properties and hydrometeorological forcing is presented using a stochastic approach (subset simulation). The sensitivity test suggests that the albedo, Bowen ratio and bulk transfer coefficient, solar radiation and wind speed are most critical. AnOHM, driven by local meteorological conditions at five sites with different land use, is shown to simulate the ΔQ[subscript S] flux well (RMSE values of ∼ 30 W m[superscript −2]). The intra-annual dynamics of OHM coefficients are explored. AnOHM offers significant potential to enhance modelling of the surface energy balance over a wider range of conditions and land covers.

We develop a general framework to analyze the controllability of multiplex networks using multiple-relation networks and multiple-layer networks with interlayer couplings as two classes of prototypical systems. In the former, networks associated with different physical variables share the same set of nodes and in the latter, diffusion processes take place. We find that, for a multiple-relation network, a layer exists that dominantly determines the controllability of the whole network and, for a multiple-layer network, a small fraction of the interconnections can enhance the controllability remarkably. Our theory is generally applicable to other types of multiplex networks as well, leading to significant insights into the control of complex network systems with diverse structures and interacting patterns.