Large-scale Complex Systems Research Articles

A novel tensor-based modal decomposition method for reduced order modeling and optimal sparse sensor placement

Data-driven modal decomposition methods have become fundamental algorithms in constructing reduced order models (ROMs), which serve as a critical tool in the digital twin modeling of large complex systems. Classical modal decomposition methods are particularly adept at handling matrix-form input data that contains columnized snapshots without retaining spatial structure and neighborhood information. Focusing on addressing this problem, this paper proposed a Tensor-Based Modal Decomposition (TBMD) method to deal with the high-order tensor-form input data. TBMD method is effective in finding the potential low-order manifolds of a given high-order dynamical system with low energy loss within fewer decomposited modes. Moreover, TBMD is extended to be used in the field of optimal sensor placement via the development of a tensor-based QR factorization method and a tensor-based compressive sensing algorithm. The tensor-based QR factorization method is able to consider the spatial structure and neighborhood information of the basis matrices data in tensor form. This is the first time that a compressive sensing model has been discussed and solved for the frontal slice-wise product of a tensor and a vector. Subsequently, comprehensive case studies are conducted based on the cylinder wake dataset, airfoil vortex dataset, sea surface temperature dataset, eigenfaces dataset and turbulent channel flow dataset. The experiment results show that TBMD can accurately capture lower-order manifolds, and the tensor-based QR factorization is effective in optimizing sensor placement with fewer sensors while maintaining high reconstruction accuracy. The proposed methods in this paper is promising to be utilized in the construction of large-scale complex system digital twins, in which TBMD can serve as a fundamental algorithm in constructing ROM.

Asymptotic optimality of a class of controlled non-Markov processes

Motivated by applications in power systems and problems arising in simulation of large scale complex system optimizations, this work is concerned with controlled stochastic switching systems. The system of interest displays a multi-time scale structure. In contrast to the so-called singularly perturbed diffusions and multi-scale Markov decision processes, controlled non-Markov processes (also known as non-Markov decision processes) are treated. The novelty of our work is the treatment of the non-Markov controlled processes and the time-scale used. The fast and slow processes are coupled through a stochastic differential equation. Using averaging, it is first shown that the non-Markov switching process has a weak limit that is a Markov decision process. Then asymptotic optimal control of the non-Markov process is obtained by using the limit process.

Chimeric states induced by higher-order interactions in coupled prey-predator systems.

Higher-order interactions have been instrumental in characterizing the intricate complex dynamics in a diverse range of large-scale complex systems. Our study investigates the effect of attractive and repulsive higher-order interactions in globally and non-locally coupled prey-predator Rosenzweig-MacArthur systems. Such interactions lead to the emergence of complex spatiotemporal chimeric states, which are otherwise unobserved in the model system with only pairwise interactions. Our model system exhibits a second-order transition from a chimera-like state (mixture of oscillating and steady state nodes) to a chimera-death state through a supercritical Hopf bifurcation. The origin of these states is discussed in detail along with the effect of the higher-order non-local topology which leads to the rise of a distinct and dynamical state termed as "amplitude-mediated chimera-like states." Our study observes that the introduction of higher-order attractive and repulsive interactions exhibit incoherence and promote persistence in consumer-resource population dynamics as opposed to susceptibility shown by synchronized dynamics with only pairwise interactions, and these results may be of interest to conservationists and theoretical ecologists studying the effect of competing interactions in ecological networks.

Boosting Progressive Optimality Algorithm Performance in Optimizing Complex Large-Scale Multi-Reservoir System Operations by Using Discrepant Optimization Windows and Disturbance-Response Strategy

Boosting Progressive Optimality Algorithm Performance in Optimizing Complex Large-Scale Multi-Reservoir System Operations by Using Discrepant Optimization Windows and Disturbance-Response Strategy

Frequency-Histogram Coarse Graining in Elementary and 2-Dimensional Cellular Automata

Cellular automata and other discrete dynamical systems have long been studied as models of emergent complexity. Recently, neural cellular automata have been proposed as models to investigate the emerge of a more general artificial intelligence, thanks to their propensity to support properties such as self-organization, emergence, and open-endedness. However, understanding emergent complexity in large scale systems is an open challenge. How can the important computations leading to emergent complex structures and behaviors be identified? In this work, we systematically investigate a form of dimensionality reduction for 1-dimensional and 2-dimensional cellular automata based on coarse-graining of macrostates into smaller blocks. We discuss selected examples and provide the entire exploration of coarse graining with different filtering levels in the appendix (available also digitally at this link: https://s4nyam.github.io/eca88/. We argue that being able to capture emergent complexity in AI systems may pave the way to open-ended evolution, a plausible path to reach artificial general intelligence.

A Massively Parallel Spatially Resolved Stochastic Cluster Dynamics Method for Simulations of Irradiated Materials

The spatially resolved stochastic cluster dynamics (SRSCD) method is one of the most important methods for simulating the time-evolution of spatially correlated microstructures in irradiated materials. It is a kinetic Monte Carlo-based method for stochastically evolving integer-valued populations of defects within multi finite volume elements according to reaction rates. However, the increasing spatial scale and complexity of the simulated systems have exceeded the capabilities of serial SRSCD. To extend SRSCD to simulate large-scale complex systems, we propose a massively parallel SRSCD method in this paper and implement the program named MISA-SLSCD. It contains four contributions: (1) a dynamic Defect-Reaction Tree data structure to efficiently store and update millions of defect species and reactions; (2) a double-grouping search strategy to speed up the search for defects and reactions; (3) an adaptive synchronous algorithm to balance the accuracy and efficiency of simulations; and (4) an on-demand communication strategy to eliminate communication redundancy. A series of numerical simulation results show that our method has high accuracy in simulating damage accumulation in irradiated materials and obtains a good performance of over 90% parallel efficiency on 32,000 CPU cores with a 32 million-volume-element system. Program summaryProgram Title: MISA-SLSCDCPC Library link to program files:https://doi.org/10.17632/ctmn5f5bzk.1Code Ocean capsule:https://codeocean.com/capsule/8351487Licensing provisions: BSD 3-clauseProgramming language: Fortran90Nature of problem: Behaviours of defects in irradiated materials are typically space-dependent long-term dynamical processes, and spanning multiple orders of magnitude in space (∼nm to m) and time (∼ μs to years). The SRSCD method is an important method for simulating the time-evolution of spatially correlated microstructures in irradiated materials. However, the increasing spatial scale and complexity of the simulated systems have exceeded the capabilities of serial and existing parallel versions. It is necessary to develop new massively parallel method and program to extend SRSCD to simulate large-scale complex systems.Solution method: To extend SRSCD to simulate large-scale complex systems, we propose a massively parallel SRSCD method in this paper and implement the program named MISA-SLSCD. It contains: (1) a dynamic Defect-Reaction Tree data structure to efficiently store and update millions of defect species and reactions; (2) a double-grouping search strategy to speed up the search for defects and reactions; (3) an adaptive synchronous algorithm to balance the accuracy and efficiency of simulations; and (4) an on-demand communication strategy to eliminate communication redundancy.

A global heat flow topology for revealing the synergistic effects of heat transfer and thermal power conversion in large scale systems: Methodology and case study

In the processes of modeling, optimization, heat transfer analysis, and thermal power conversion analysis in large-scale complex systems, challenges are encountered regarding intricate design and optimization. In this paper, a global heat flow topology is proposed that reveals the synergistic effect of heat transfer and thermal power conversion in large-scale systems. The approach involves the construction of a heat flow topology, mathematical model development, analysis of specific parameters, and multi-objective optimization. It integrates various methods and tools, including heat flow topology, numerical simulation tool, entransy transfer analysis, global thermal resistances visualization, genetic algorithm (GA), and technique for order preference by similarity to ideal solution (TOPSIS). By using the LNG cold energy recovery and regasification process in floating storage and regasification units (FSRU) as a case study, the methodology provides a comprehensive understanding of the interactions and conversions between heat transfer and thermal power within the system. The results demonstrate that the optimal configuration achieves a maximum thermal efficiency of 21.73 % and a maximum cold recovery efficiency of 58.48 %. Additionally, the interaction between LNG pressure and seawater temperature exhibits limited synergistic impact on both LNG cold exergy efficiency and entransy transfer efficiency. Similarly, the combined influence of LNG pressure and evaporator temperature shows weak synergistic effects on parameters such as supply/demand ratio, thermal efficiency, and entransy transfer efficiency. The analysis of the global thermal resistance bubble chart reveals the significant impact of evaporator temperature and seawater temperature on the evaporator resistance, as well as the notable impact of LNG pressure on the condenser resistance.

Leveraging pervasive computing for ambient intelligence: A survey on recent advancements, applications and open challenges

The advent of pervasive computing and ambient intelligence has opened up new possibilities for the development of intelligent systems that can adapt to the needs of their users. This paper provides a comprehensive survey of recent advancements in this field, focusing on the different techniques, tools, and applications that have been developed to leverage the benefits of pervasive computing for ambient intelligence. We discuss the various challenges and opportunities that arise when designing such systems and provide a critical evaluation of the current state-of-the-art. In particular, we examine the role of machine learning, data analytics, and context awareness in the development of intelligent systems, and we investigate the impact of these technologies on the quality of life of their users. The paper also presents a discussion on the open challenges in this field, such as ensuring user privacy, security, and trust, designing effective user interfaces, and dealing with the complexity of large-scale systems. Finally, we conclude with an outlook on the future of ambient intelligence and its potential impact on society, and we highlight some of the emerging research directions that are likely to shape the field in the coming years.

LogRule: Efficient Structured Log Mining for Root Cause Analysis

Accurate, timely Root Cause Analysis (RCA) is essential to successful IT operations as a primary step to incident remediation. RCA automation using data mining techniques in large heterogeneous systems is, however, a challenging task, because it requires correlating multimodal information across various data sources. An increasing number of services are migrating to structured logging to enable automated monitoring and debugging of complex large-scale systems. In this paper, we leverage structured logs and association rule mining (ARM) to automate RCA. We propose the LogRule algorithm, which automatically analyzes structured logs to generate a list of explanations for an event of interest. It achieves 0.921 F1-score for the diagnosis task, while computing results 37x faster compared to the state-of-the-art solution based on FP-growth, making it a time-efficient, accurate, and interpretable ARM-based RCA algorithm. Evaluation results show that LogRule enables RCA in complex multidimensional datasets, where the execution time of the current state-of-the-art algorithm is prohibitively large.

Uncovering misalignments in the health- and elderly care system

PurposeThe increasing size of the elderly population is emerging as a primary catalyst for the escalation of healthcare expenditure, and a sense of urgency is manifest. However, the complexity of the health- and elderly care systems provides challenges in improving system efficiency. Hence, the system-level understanding of the main obstacles to integration care needs further exploration. In order to better integrate health- and elderly care, the study needs to identify the actual misalignments underpinning the issue. This study provides the theoretical foundations for resource misalignments and provides empirical examples of these.Design/methodology/approachSemi-structured interviews with multiple stakeholders on various hierarchical levels were carried out to create a more complete view of the system and resources deployed in health- and elderly care. The application of user-centered design methods and co-creation with employees have also been crucial to the outcomes of the study.FindingsResults show that health- and elderly care is a large-scale complex system. The overlapping and mutually reinforcing misalignments are: (1) regulation and policy differences, (2) stakeholder quantity and variation, (3) external control of health- and elderly care, (3) decreasing collaboration and (4) communication channels and IT development.Originality/valueThis qualitative study builds on institutional theory and resource integration theory and contributes with empirical descriptions of misalignments in the health- and elderly care system. These descriptions will serve as points of departure for systems design to improve the efficiency and effectiveness of health- and elderly care.

Multi-fidelity Bayesian learning for offshore production well reliability analysis

The complexity of large-scale systems has greatly increased and the tools for monitoring complex systems and ensuring their reliability have also been vastly studied. This study presents a tool to improve the understanding of the state-of-health of complex systems with several subsystems and components and partial observability. Many real systems can have issues with monitoring due to the cost of monitoring or physical limitations for constant monitoring. The proposed method in this study tackles this issue by utilizing all available, limited data to better predict system performance. A Multi-scale Multi-Fidelity Bayesian Learning framework is proposed to provide an online tool for component failure prediction as well as system prognostics. A Dynamic Bayesian Network is used to model the dependencies between components in a system and their impact on system failure. Multi-Fidelity Networks is then used to continuously update the parameters in the network to leverage online data to improve the predictive results. A case study of the leakage failure mode of an offshore production well is conducted to highlight the applicability as well as the benefit of the proposed approach. The results show the advantage of accounting for high-fidelity data to inform low-fidelity data for complex systems.

A reduced order model based on adaptive proper orthogonal decomposition incorporated with modal coefficient learning for digital twin in process industry

The digital twin (DT) technology provides a viable and promising direction for improving the level of the production status monitoring and the overall product quality in various fields. However, the accuracy of working condition identification, the timeliness of process adjustment, and the stability of product quality are put forward higher requirements in the process industry, which is characterized by nonlinear, large-scale, and dynamic complex systems. Therefore, it still remains a tricky challenge to construct and maintain an effective and accurate DT model in the process industry. A reduced order model (ROM) with the adaptive updating ability is proposed. The adaptive proper orthogonal decomposition (APOD) is adopted to achieve the continuous iteration and the adaptive optimization of the reduced basis set. Correspondingly, an adaptive learning algorithm based on the least squares support vector regression (LS-SVR) is developed to quickly obtain the modal coefficients and effectively circumvent the prohibitively high computational cost. In this way, the physical field of interest is expressed in a low-dimensional approximation with a high accuracy. The effectiveness of the method is verified by a case study in the process industry. Results show that the proposed model displays a high-precision fitting and a significant time saving for the full order model (FOM).

G-DCS: GCN-Based Deep Code Summary Generation Model

<p>In software engineering, software personnel faced many large-scale software and complex systems, these need programmers to quickly and accurately read and understand the code, and efficiently complete the tasks of software change or maintenance tasks. Code-NN is the first model to use deep learning to accomplish the task of code summary generation, but it is not used the structural information in the code itself. In the past five years, researchers have designed different code summarization systems based on neural networks. They generally use the end-to-end neural machine translation framework, but many current research methods do not make full use of the structural information of the code. This paper raises a new model called G-DCS to automatically generate a summary of java code; the generated summary is designed to help programmers quickly comprehend the effect of java methods. G-DCS uses natural language processing technology, and training the model uses a code corpus. This model could generate code summaries directly from the code files in the coded corpus. Compared with the traditional method, it uses the information of structural on the code. Through Graph Convolutional Neural Network (GCN) extracts the structural information on the code to generate the code sequence, which makes the generated code summary more accurate. The corpus used for training was obtained from GitHub. Evaluation criteria using BLEU-n. Experimental results show that our approach outperforms models that do not utilize code structure information.</p> <p> </p>

A Review of High-Performance Computing Methods for Power Flow Analysis

Power flow analysis is critical for power systems due to the development of multiple energy supplies. For safety, stability, and real-time response in grid operation, grid planning, and analysis of power systems, it requires designing high-performance computing methods, accelerating power flow calculation, obtaining the voltage magnitude and phase angle of buses inside the power system, and coping with the increasingly complex large-scale power system. This paper provides an overview of the available parallel methods to fix the issues. Specifically, these methods can be classified into three categories from a hardware perspective: multi-cores, hybrid CPU-GPU architecture, and FPGA. In addition, from the perspective of numerical computation, the power flow algorithm is generally classified into iterative and direct methods. This review paper introduces models of power flow and hardware computing architectures and then compares their performance in parallel power flow calculations depending on parallel numerical methods on different computing platforms. Furthermore, this paper analyzes the challenges and pros and cons of these methods and provides guidance on how to exploit the parallelism of future power flow applications.

Perspective on machine learning in energy material discovery

Perspective on machine learning in energy material discovery

Models and Methods of Designing Data-Centric Microservice Architectures of Digital Enterprises

The article is devoted to methods and models of designing systems for the digital transformation of industrial enterprises within the framework of the Industry 4.0 concept. The purpose of this work is to formalize a new notation for graphical modeling of the architecture of complex large-scale systems with data-centric microservice architectures and to present a variant of the reference model of such an architecture for creating an autonomously functioning industrial enterprise. The paper provides a list and justification for the use of functional components of a data-centric microservice architecture based on the analysis of modern approaches to building systems and the authors’ own results obtained during the implementation of a number of projects. The problems of using traditional graphical modeling notations to represent a data-centric microservice architecture are considered. Examples of designing a model of such an architecture for a mining enterprise are given.

SOLVING HIGH-DIMENSIONAL INVERSE PROBLEMS WITH AUXILIARY UNCERTAINTY VIA OPERATOR LEARNING WITH LIMITED DATA

In complex large-scale systems such as climate, important effects are caused by a combination of confounding processes that are not fully observable. The identification of sources from observations of the system state is vital for attribution and prediction, which inform critical policy decisions. The difficulty of these types of inverse problems lies in the inability to isolate sources and the cost of simulating computational models. Surrogate models may enable the many-query algorithms required for source identification, but data challenges arise from high dimensionality of the state and source, limited ensembles of costly model simulations to train a surrogate model, and few and potentially noisy state observations for inversion due to measurement limitations. The influence of auxiliary processes adds an additional layer of uncertainty that further confounds source identification. We introduce a framework based on (1) calibrating deep neural network surrogates to the flow maps provided by an ensemble of simulations obtained by varying sources, and (2) using these surrogates in a Bayesian framework to identify sources from observations via optimization. Focusing on an atmospheric dispersion exemplar, we find that the expressive and computationally efficient nature of the deep neural network operator surrogates in appropriately reduced dimension allows for source identification with uncertainty quantification using limited data. Introducing a variable wind field as an auxiliary process, we find that a Bayesian approximation error approach is essential for reliable source inversion when uncertainty due to wind stresses the algorithm.

Guest Editorial: Learning, optimisation and control of cyber‐physical systems

Guest Editorial: Learning, optimisation and control of cyber‐physical systems

Coordinated Control of Networked Nonlinear Multiagent Systems Using Variable Horizon Learning Predictors via Cloud Edge Computing

Networked multiagent systems use network technology to realize the interconnection, intercommunication, and mutual control of things. The coordinated control problem of networked nonlinear multiagent systems via cloud edge computing is investigated in this article. A mist–fog–cloud predictive control scheme is proposed for the coordinated control of complex large-scale networked multiagent systems by making use of the advantages of cloud edge computing. This scheme actively compensates for communication delays and achieves desired coordination performance of individual agents. Variable horizon learning predictors are presented to predict the outputs of the unknown nonlinear dynamical agents within different horizons. The design of coordinated control optimizes a performance index function presented to measure the coordination between agents. The analysis on a networked nonlinear multiagent system using the mist–fog–cloud predictive control scheme results in the conditions of simultaneous consensus and stability of the entire closed-loop system. An example demonstrates the effectiveness of the proposed scheme.

Planning of distributed renewable energy systems under uncertainty based on statistical machine learning

The development of distributed renewable energy, such as photovoltaic power and wind power generation, makes the energy system cleaner, and is of great significance in reducing carbon emissions. However, weather can affect distributed renewable energy power generation, and the uncertainty of output brings challenges to uncertainty planning for distributed renewable energy. Energy systems with high penetration of distributed renewable energy involve the high-dimensional, nonlinear dynamics of large-scale complex systems, and the optimal solution of the uncertainty model is a difficult problem. From the perspective of statistical machine learning, the theory of planning of distributed renewable energy systems under uncertainty is reviewed and some key technologies are put forward for applying advanced artificial intelligence to distributed renewable power uncertainty planning.