Simulation Of Complex Systems Research Articles

Mining weighted subgraphs in a single large graph

Weighted single large graphs are often used to simulate complex systems, and thus mining frequent subgraphs in a weighted large graph is an important issue that has attracted the attention of many researchers. Within the studies on the mining of frequent subgraphs, the Graph Mining (GraMi) algorithm is considered as a state-of-the-art algorithm. However, this algorithm can only be implemented on graph datasets which have node labels and edge labels, without weights, and this means that the importance of all objects in a large graph is the same. In this paper, we propose a frequent subgraph algorithm on a weighted large graph, called Weighted Graph Mining (WeGraMi), which is based on two effective strategies to mining weighted subgraphs. Firstly, we apply a new strategy to calculate the weight of all mined subgraphs, which is based on the weights of the nodes in that subgraph. Secondly, we apply a search space pruning strategy based on the existing weights; if a frequent subgraph cannot satisfy the given weighting threshold, that subgraph will be pruned, which can reduce the processing time and storage space needed. With both directed and undirected graph datasets, our experimental results show that the runtime as well as the memory requirements of our algorithm are significantly better than those of GraMi.

Improved GPU near neighbours performance for multi-agent simulations

Complex systems simulations are well suited to the SIMT paradigm of GPUs, enabling millions of actors to be processed in fractions of a second. At the core of many such simulations, fixed radius near neighbours (FRNN) search provides the actors with spatial awareness of their neighbours. The FRNN search process is frequently the limiting factor of performance, due to the disproportionate level of scattered memory reads demanded by the query stage, leading to FRNN search runtimes exceeding that of simulation logic. In this paper, we propose and evaluate two novel optimisations (Strips and Proportional Bin Width) for improving the performance of uniform spatially partitioned FRNN searches and apply them in combination to demonstrate the impact on the performance of multi-agent simulations. The two approaches aim to reduce latency in search and reduce the amount of data considered (i.e. more efficient searching), respectively. When the two optimisations are combined, the peak obtained speedups observed in a benchmark model are 1.27x and 1.34x in two and three dimensional implementations, respectively. Due to additional non FRNN search computation, the peak speedup obtained when applied to complex system simulations within FLAMEGPU is 1.21x.

Comparison of uncertainty quantification process using statistical and data mining algorithms

Uncertainty quantification has always been an important topic in model reduction and simulation of complex systems. In this aspect, global sensitivity analysis (GSA) methods such as Fourier amplitude sensitivity test (FAST) are well recognized as effective algorithms. Recently, some data-based metamodeler such as Random Forest (RF) also developed their own variable importance selection solutions for parameters with perturbations. This paper proposes a visual comparison of these two uncertainty quantification methods, using datasets retrieved from vibroacoustic models. Their results have a lot in common and are capable to explain many results. The remarkable agreement between methods under fundamentally different definitions can potentially improve their compatibility in various occasions.

Dimensional Quantum Memory Advantage in the Simulation of Stochastic Processes

Stochastic processes underlie a vast range of natural and social phenomena. Some processes such as atomic decay feature intrinsic randomness, whereas other complex processes, e.g. traffic congestion, are effectively probabilistic because we cannot track all relevant variables. To simulate a stochastic system's future behaviour, information about its past must be stored and thus memory is a key resource. Quantum information processing promises a memory advantage for stochastic simulation that has been validated in recent proof-of-concept experiments. Yet, in all past works, the memory saving would only become accessible in the limit of a large number of parallel simulations, because the memory registers of individual quantum simulators had the same dimensionality as their classical counterparts. Here, we report the first experimental demonstration that a quantum stochastic simulator can encode the relevant information in fewer dimensions than any classical simulator, thereby achieving a quantum memory advantage even for an individual simulator. Our photonic experiment thus establishes the potential of a new, practical resource saving in the simulation of complex systems.

A System Dynamics Modeling Support System Based on Computational Intelligence

System dynamics (SD) is a complex systems modeling and simulation approach with wide ranging applications in various science and engineering disciplines. While subject matter experts lead most of the model building, recent advances have attempted to bring system dynamics closer to fast growing fields such as data sciences. This may prove promising for the development of novel support methods that augment human cognition and improve efficiencies in the model building process. A few different directions have been explored recently to support individual modeling stages, such as the generation of model structure, model calibration and policy optimization. However, an integrated approach that supports across the board modeling process is still missing. In this paper, a prototype integrated modeling support system is presented for the purpose of supporting the modelers at each stage of the process. The proposed support system facilitates data-driven inferring of causal loop diagrams (CLDs), stock-flow diagrams (SFDs), model equations and the estimation of model parameters using computational intelligence (CI) techniques. The ultimate goal of the proposed system is to support the construction of complex models, where the human power is not enough. With this goal in mind, we demonstrate the working and utility of the proposed support system. We have used two well-known synthetic reality case studies with small models from the system dynamics literature, in order to verify the support system performance. The experimental results showed the effectiveness of the proposed support system to infer close model structures to target models directly from system time-series observations. Future work will focus on improving the support system so that it can generate complex models on a large scale.

A framework for FMI-based co-simulation of human–machine interfaces

A framework for co-simulation of human–machine interfaces in Cyber-Physical Systems (CPS) is presented. The framework builds on formal (i.e. mathematical) methods. It aims to support the work of formal methods experts in charge of modelling and analysing safety-critical aspects of user interfaces in CPS. To carry out these modelling and analysis activities, formal methods experts usually need to engage with domain experts that may not fully understand the mathematical details of formal analysis results. The framework presented in this work mitigates this communication barrier by allowing formal methods experts to create interactive prototypes driven by formal models. The prototypes closely resemble the visual appearance of the system being developed. They can be used to discuss details of the formal analysis effort without showing any mathematical detail. An existing prototyping toolkit based on formal methods is used as baseline technology. Novel functionalities are developed for automatic generation of interactive prototypes supporting the Functional Mockup Interface (FMI), a de-facto standard technology for simulation of complex systems. Using the FMI interface, the prototypes can be integrated with simulations of other system components. The architecture of the framework is presented, along with a verification of core aspects of its functionalities. A case study based on a medical system is used to demonstrate the capabilities of the framework.

Service-Oriented Model Encapsulation and Selection Method for Complex System Simulation Based on Cloud Architecture

With the rise in cloud computing architecture, the development of service-oriented simulation models has gradually become a prominent topic in the field of complex system simulation. In order to support the distributed sharing of the simulation models with large computational requirements and to select the optimal service model to construct complex system simulation applications, this paper proposes a service-oriented model encapsulation and selection method. This method encapsulates models into shared simulation services, supports the distributed scheduling of model services in the network, and designs a semantic search framework which can support users in searching models according to model correlation. An optimization selection algorithm based on quality of service (QoS) is proposed to support users in customizing the weights of QoS indices and obtaining the ordered candidate model set by weighted comparison. The experimental results showed that the parallel operation of service models can effectively improve the execution efficiency of complex system simulation applications, and the performance was increased by 19.76% compared with that of scatter distribution strategy. The QoS weighted model selection method based on semantic search can support the effective search and selection of simulation models in the cloud environment according to the user’s preferences.

Guest Editorial: Energy Internet and its Related Technology Research

Guest Editorial: Energy Internet and its Related Technology Research

Towards domain-specific surrogate models for smart grid co-simulation

Surrogate models are used to reduce the computational effort required to simulate complex systems. The power grid can be considered as such a complex system with a large number of interdependent inputs. With artificial neural networks and deep learning, it is possible to build high-dimensional approximation models. However, a large data set is also required for the training process. This paper presents an approach to sample input data and create a deep learning surrogate model for a low voltage grid. Challenges are discussed and the model is evaluated under different conditions. The results show that the model performs well from a machine learning point of view, but has domain-specific weaknesses.

A GPU Algorithm for Agent-Based Models to Simulate the Integration of Cell Membrane Signals

Simulation of complex biological systems with agent-based models is becoming more relevant with the increase in Graphics Processing Unit (GPU) power. In those simulations, up to millions of virtual cells are individually computed, involving daunting processing times. An important part of computational models is the algorithm that manages how agents perceive their surroundings. This can be particularly problematic in three-dimensional environments where agents have deformable virtual membranes. This article presents a GPU algorithm that gives the possibility for agents to integrate the signals scattered on their virtual membrane. It is detailed to be coded in languages like OpenCL or Cuda. Its performances are tested to show its speed with current GPU devices. Finally, it was implemented inside an existing software to test and illustrate the possibilities it offers.

High school students’ epistemological approaches to computer simulations of complex systems

The science of complex systems can provide not only scientist, but also professionals, policy-makers and citizens, with thinking resources to interpret and understand most of the modern global challenges. In this field, the widespread use of computational simulations, that are neither theoretical instruments nor laboratory experiments, has been contributing to the widening of the scientific skill gap between experts and citizens. The pilot study we present in this contribution aims at investigating high school students’ approaches towards simulations of complex systems, by searching for the criteria they use to evaluate their explanatory power and the reliability of their results. Preliminary analysis of the paired interviews has shown that (1) rarely students are able to elaborate explanations of the simulated complex phenomena, and (2) their critical attitude and trust towards simulations are strongly affected by their epistemological background. We argue that these findings deserve to be furtherly investigated, to understand in more details the sources of students’ difficulties in recognizing the epistemological and methodological value of simulations for scientific research and practice.

Understanding Missing Entropy in Coarse-Grained Systems: Addressing Issues of Representability and Transferability.

Coarse-grained (CG) models facilitate efficient simulation of complex systems by integrating out the atomic, or fine-grained (FG), degrees of freedom. Systematically derived CG models from FG simulations often attempt to approximate the CG potential of mean force (PMF), an inherently multidimensional and many-body quantity, using additive pairwise contributions. However, they currently lack fundamental principles that enable their extensible use across different thermodynamic state points, i.e., transferability. In this work, we investigate the explicit energy-entropy decomposition of the CG PMF as a means to construct transferable CG models. In particular, despite its high-dimensional nature, we find for liquid systems that the entropic component to the CG PMF can similarly be represented using additive pairwise contributions, which we show is highly coupled to the CG configurational entropy. This approach formally connects the missing entropy that is lost due to the CG representation, i.e., translational, rotational, and vibrational modes associated with the missing degrees of freedom, to the CG entropy. By design, the present framework imparts transferable CG interactions across different temperatures due to the explicit definition of an additive entropic contribution. Furthermore, we demonstrate that transferability across composition state points, such as between bulk liquids and their mixtures, is also achieved by designing combining rules to approximate cross-interactions from bulk CG PMFs. Using the predicted CG model for liquid mixtures, structural correlations of the fitted CG model were found to corroborate a high-fidelity combining rule. Our findings elucidate the physical nature and compact representation of CG entropy and suggest a new approach for overcoming the transferability problem. We expect that this approach will further extend the current view of CG modeling into predictive multiscale modeling.

Low cost DSP‐based educational embedded platform for real‐time simulation and fast implementation of complex systems in Simulink

AbstractIn this paper, we present a teaching methodology based on a low‐cost embedded system platform that is easy to use and replicate, oriented to real‐time simulation project development. Designed for real‐time implementation of coding generated with MPLAB Device Blocks for Simulink. The purpose of this methodology is that students, teachers, and professionals will be able to carry out a theoretical project of a simulation in a computer, which includes complex mathematical modeling, to interact with its environment in real‐time. This system has been utilized already by students of Engineering of the first semesters as well as professors and researchers in updated and initialization courses in embedded systems within the University of Guadalajara. Finally, in the results section, we show a comparison of the proposed platform and a dSPACE platform applied in a control problem with an intricate mathematical model to process through a Hardware‐in‐the‐loop (HIL).

Simulation of Complex Systems Using the Observed Data Based on Recurrent Artificial Neural Networks

We propose a new approach to reconstructing complex, spatially distributed systems on the basis of the time series generated by such systems. It allows one to combine two basic steps of such a reconstruction, namely, the choice of a set of phase variables of the system using the observed time series and the development of the evolution operator acting in the chosen phase space with the help of an artificial neural network with special topology. This network, first, maps the initial high-dimensional data onto the lower-dimension space and, second, specifies the evolution operator in this space. The efficiency of this approach is demonstrated by an example of reconstructing the Lorenz system representing a high-dimensional model of atmospheric dynamics.

Firefly artificial intelligence technique for model order reduction with substructure preservation

Model order reduction (MOR) is a process of finding a lower order model for the original high order system with reasonable accuracy in order to simplify analysis, design, modeling and simulation for large complex systems. It is desirable that the reduced order model preserves the fundamental properties of the original system. This paper presents a new MOR technique of multi-input multi-output systems utilizing the firefly algorithm (FA) as an artificial intelligence technique. The reduction operation is proposed to maintain the exact dominant dynamics in the reduced order model with the advantage of substructure preservation. This is mainly possible for systems that are characterized as multi-time scale systems. Obtaining the reduced order model is achieved by minimizing the fitness function that is related to the error between the full and reduced order models’ responses. The new approach is compared with recently published work on firefly optimization for MOR, in addition to three other artificial intelligence techniques; namely, invasive weed optimization, particle swarm optimization and genetic algorithm. As a result, simulations show the potential of the FA for the process of MOR.

Strongly correlated quantum walks with a 12-qubit superconducting processor.

Quantum walks are the quantum analogs of classical random walks, which allow for the simulation of large-scale quantum many-body systems and the realization of universal quantum computation without time-dependent control. We experimentally demonstrate quantum walks of one and two strongly correlated microwave photons in a one-dimensional array of 12 superconducting qubits with short-range interactions. First, in one-photon quantum walks, we observed the propagation of the density and correlation of the quasiparticle excitation of the superconducting qubit and quantum entanglement between qubit pairs. Second, when implementing two-photon quantum walks by exciting two superconducting qubits, we observed the fermionization of strongly interacting photons from the measured time-dependent long-range anticorrelations, representing the antibunching of photons with attractive interactions. The demonstration of quantum walks on a quantum processor, using superconducting qubits as artificial atoms and tomographic readout, paves the way to quantum simulation of many-body phenomena and universal quantum computation.

A novel simplified modeling method based on R–Q curve of resistive type SFCL in power systems

Resistive type superconducting fault current limiter (R-SFCL) has always been a research hotspot. Due to its simple principle and structure, good current limiting effect and easy module, it has a promising prospect applied in power system. However, R-SFCL quenching is a complex process of multiple physical fields coupling, including current, magnetic field and temperature. In general, finite element software is used to accurately simulate the process of R-SFCL. In the simulation of complex power system, the coupling calculation method can increase the complexity, calculated amount and operation time of transient simulation, availability of which is low. The calculation method of fitting formula can't accurately reflect the change of resistance in the quenching process, which affects the accuracy of system simulation. According to experiments and simulations, it is found that once superconducting tape tends to completely quenching, the quenching resistance R of YBCO superconducting tape has a fixed relation with joule heat Q produced by quenching resistance when the refrigerating mode remains unchanged. Therefore a novel simplified modeling method based on R–Q curve of YBCO resistive type SFCL is proposed, which can be easily implemented in system simulation software such as MATLAB, PSCAD/EMTDC. This method is simple structure and based on experimental data which has the advantage of fast operation speed. The good simulation effect of the method is verified by quenching experiments.

Communication Sparsity in Distributed Spiking Neural Network Simulations to Improve Scalability.

In the last decade there has been a surge in the number of big science projects interested in achieving a comprehensive understanding of the functions of the brain, using Spiking Neuronal Network (SNN) simulations to aid discovery and experimentation. Such an approach increases the computational demands on SNN simulators: if natural scale brain-size simulations are to be realized, it is necessary to use parallel and distributed models of computing. Communication is recognized as the dominant part of distributed SNN simulations. As the number of computational nodes increases, the proportion of time the simulation spends in useful computing (computational efficiency) is reduced and therefore applies a limit to scalability. This work targets the three phases of communication to improve overall computational efficiency in distributed simulations: implicit synchronization, process handshake and data exchange. We introduce a connectivity-aware allocation of neurons to compute nodes by modeling the SNN as a hypergraph. Partitioning the hypergraph to reduce interprocess communication increases the sparsity of the communication graph. We propose dynamic sparse exchange as an improvement over simple point-to-point exchange on sparse communications. Results show a combined gain when using hypergraph-based allocation and dynamic sparse communication, increasing computational efficiency by up to 40.8 percentage points and reducing simulation time by up to 73%. The findings are applicable to other distributed complex system simulations in which communication is modeled as a graph network.

Enhancing Biochemistry Lab with Arduino

The advancement of the new technologies of information and communication brought significant contributions to science education. The most remarkable and widespread technology is handheld computers, such as smartphones. Some examples of the benefits brought by personal computers to science education are the possibility of interaction with models and their development, the capacity of data storage, treatment, exchange, and visualization as well as the possibility to simulate complex systems. The capacity of having things (instruments, equipment, sensors, actuators) connected to the internet opened a unique perspective for science education that is the possibility of massively teaching science by doing science, even in online courses. Experiments can be planned, and data can be collected and interpreted remotely, using personal devices and equipment that can be made available to students for remote operation, from their schools.We have developed Arduino based pieces of equipment to enhance biochemistry lab optimizing data collection and treatment processes. In this work, it will be presented the results obtained from an online automatic titrator and a low‐cost spectrophotometer. The automatic online titrator (Figure 1) is composed of two scales on which stock solutions of titrants are deposited. The titrant solutions are added to the titrated using peristaltic pumps. A magnetic stirrer plate on which is placed a Becker containing the titrated, having its base illuminated for better visualization of the titration reactions. A pH meter is immersed into the titrated. All measurements of the sensors (scale and pH) are broadcasted online to a web page. The peristaltic pumps and magnetic stirrer controllers are also made online through the same page. Among the experiments that can be performed using this equipment is the amino acid, buffer, and pH indicators titrations.The low‐cost spectrophotometer (Figure 2) was constructed by using an RGB LED and a light detector. The RGB LED can be set to deliver visible light at various wavelengths. Among the experiments that can be performed with this equipment is the dosage of proteins and glucose. We used the low‐cost spectrophotometer for protein dosage by the Bradford method, and for glucose dosage by the Somogyi‐Nelsen method. The results obtained showed a correlation higher than 0.98 with the same measurement made in a commercial spectrophotometer.The use of Arduino technology has enormous potential to increase biochemistry laboratories. Among the equipment developed, the online automatic titrator allows the realization of titrations in a faster way and with the automatic registration of the data. It allows students to devote more time to the planning of the experiments and interpretation of the results. The spectrophotometer developed, presented results equivalent to commercial equipment, and for its small size and battery power, it can be used in field activities. We have also taught graduate students to build their equipment to optimise the experiments they perform in their research and classes.Support or Funding InformationThis study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001, CNPq, and grant# 2016/05243‐7, São Paulo Research Foundation (FAPESP)This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Analytic Versus Computational Cognitive Models: Agent-Based Modeling as a Tool in Cognitive Sciences

Computational cognitive models typically focus on individual behavior in isolation. Models frequently employ closed-form solutions in which a state of the system can be computed if all parameters and functions are known. However, closed-form models are challenged when used to predict behaviors for dynamic, adaptive, and heterogeneous agents. Such systems are complex and typically cannot be predicted or explained by analytical solutions without application of significant simplifications. In addressing this problem, cognitive and social psychological sciences may profitably use agent-based models, which are widely employed to simulate complex systems. We show that these models can be used to explore how cognitive models scale in social networks to calibrate model parameters, to validate model predictions, and to engender model development. Agent-based models allow for controlled experiments of complex systems and can explore how changes in low-level parameters impact the behavior at a whole-system level. They can test predictions of cognitive models and may function as a bridge between individually and socially oriented models.