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Bibliografías temáticas / Complex systems, networks, dynamical models on networks, stochastic models / Artículos de revistas

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Artículos de revistas sobre el tema "Complex systems, networks, dynamical models on networks, stochastic models"

Autor: Grafiati

Publicado: 10 de marzo de 2023

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1

Rozum, Jordan C., Jorge Gómez Tejeda Zañudo, Xiao Gan, Dávid Deritei, and Réka Albert. "Parity and time reversal elucidate both decision-making in empirical models and attractor scaling in critical Boolean networks." Science Advances 7, no. 29 (2021): eabf8124. http://dx.doi.org/10.1126/sciadv.abf8124.

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We present new applications of parity inversion and time reversal to the emergence of complex behavior from simple dynamical rules in stochastic discrete models. Our parity-based encoding of causal relationships and time-reversal construction efficiently reveal discrete analogs of stable and unstable manifolds. We demonstrate their predictive power by studying decision-making in systems biology and statistical physics models. These applications underpin a novel attractor identification algorithm implemented for Boolean networks under stochastic dynamics. Its speed enables resolving a long-stan

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2

Morrison, Megan, and Lai-Sang Young. "Chaotic heteroclinic networks as models of switching behavior in biological systems." Chaos: An Interdisciplinary Journal of Nonlinear Science 32, no. 12 (2022): 123102. http://dx.doi.org/10.1063/5.0122184.

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Key features of biological activity can often be captured by transitions between a finite number of semi-stable states that correspond to behaviors or decisions. We present here a broad class of dynamical systems that are ideal for modeling such activity. The models we propose are chaotic heteroclinic networks with nontrivial intersections of stable and unstable manifolds. Due to the sensitive dependence on initial conditions, transitions between states are seemingly random. Dwell times, exit distributions, and other transition statistics can be built into the model through geometric design an

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3

Safdari, Hadiseh, Martina Contisciani, and Caterina De Bacco. "Reciprocity, community detection, and link prediction in dynamic networks." Journal of Physics: Complexity 3, no. 1 (2022): 015010. http://dx.doi.org/10.1088/2632-072x/ac52e6.

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Abstract Many complex systems change their structure over time, in these cases dynamic networks can provide a richer representation of such phenomena. As a consequence, many inference methods have been generalized to the dynamic case with the aim to model dynamic interactions. Particular interest has been devoted to extend the stochastic block model and its variant, to capture community structure as the network changes in time. While these models assume that edge formation depends only on the community memberships, recent work for static networks show the importance to include additional param

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4

KNOPOFF, D. "ON A MATHEMATICAL THEORY OF COMPLEX SYSTEMS ON NETWORKS WITH APPLICATION TO OPINION FORMATION." Mathematical Models and Methods in Applied Sciences 24, no. 02 (2013): 405–26. http://dx.doi.org/10.1142/s0218202513400137.

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This paper presents a development of the so-called kinetic theory for active particles to the modeling of living, hence complex, systems localized in networks. The overall system is viewed as a network of interacting nodes, mathematical equations are required to describe the dynamics in each node and in the whole network. These interactions, which are nonlinearly additive, are modeled by evolutive stochastic games. The first conceptual part derives a general mathematical structure, to be regarded as a candidate towards the derivation of models, suitable to capture the main features of the said

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5

Jirsa, Viktor, and Hiba Sheheitli. "Entropy, free energy, symmetry and dynamics in the brain." Journal of Physics: Complexity 3, no. 1 (2022): 015007. http://dx.doi.org/10.1088/2632-072x/ac4bec.

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Abstract Neuroscience is home to concepts and theories with roots in a variety of domains including information theory, dynamical systems theory, and cognitive psychology. Not all of those can be coherently linked, some concepts are incommensurable, and domain-specific language poses an obstacle to integration. Still, conceptual integration is a form of understanding that provides intuition and consolidation, without which progress remains unguided. This paper is concerned with the integration of deterministic and stochastic processes within an information theoretic framework, linking informat

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6

Penfold, Christopher A., and David L. Wild. "How to infer gene networks from expression profiles, revisited." Interface Focus 1, no. 6 (2011): 857–70. http://dx.doi.org/10.1098/rsfs.2011.0053.

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Inferring the topology of a gene-regulatory network (GRN) from genome-scale time-series measurements of transcriptional change has proved useful for disentangling complex biological processes. To address the challenges associated with this inference, a number of competing approaches have previously been used, including examples from information theory, Bayesian and dynamic Bayesian networks (DBNs), and ordinary differential equation (ODE) or stochastic differential equation. The performance of these competing approaches have previously been assessed using a variety of in silico and in vivo dat

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7

Parham, Paul E., and Neil M. Ferguson. "Space and contact networks: capturing the locality of disease transmission." Journal of The Royal Society Interface 3, no. 9 (2005): 483–93. http://dx.doi.org/10.1098/rsif.2005.0105.

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While an arbitrary level of complexity may be included in simulations of spatial epidemics, computational intensity and analytical intractability mean that such models often lack transparency into the determinants of epidemiological dynamics. Although numerous approaches attempt to resolve this complexity–tractability trade-off, moment closure methods arguably offer the most promising and robust frameworks for capturing the role of the locality of contact processes on global disease dynamics. While a close analogy may be made between full stochastic spatial transmission models and dynamic netw

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Warne, David J., Ruth E. Baker, and Matthew J. Simpson. "Simulation and inference algorithms for stochastic biochemical reaction networks: from basic concepts to state-of-the-art." Journal of The Royal Society Interface 16, no. 151 (2019): 20180943. http://dx.doi.org/10.1098/rsif.2018.0943.

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Stochasticity is a key characteristic of intracellular processes such as gene regulation and chemical signalling. Therefore, characterizing stochastic effects in biochemical systems is essential to understand the complex dynamics of living things. Mathematical idealizations of biochemically reacting systems must be able to capture stochastic phenomena. While robust theory exists to describe such stochastic models, the computational challenges in exploring these models can be a significant burden in practice since realistic models are analytically intractable. Determining the expected behaviour

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9

Cardelli, Luca, Isabel Cristina Perez-Verona, Mirco Tribastone, Max Tschaikowski, Andrea Vandin, and Tabea Waizmann. "Exact maximal reduction of stochastic reaction networks by species lumping." Bioinformatics 37, no. 15 (2021): 2175–82. http://dx.doi.org/10.1093/bioinformatics/btab081.

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Abstrtact Motivation Stochastic reaction networks are a widespread model to describe biological systems where the presence of noise is relevant, such as in cell regulatory processes. Unfortunately, in all but simplest models the resulting discrete state-space representation hinders analytical tractability and makes numerical simulations expensive. Reduction methods can lower complexity by computing model projections that preserve dynamics of interest to the user. Results We present an exact lumping method for stochastic reaction networks with mass-action kinetics. It hinges on an equivalence r

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10

Bombieri, Nicola, Silvia Scaffeo, Antonio Mastrandrea, et al. "SystemC Implementation of Stochastic Petri Nets for Simulation and Parameterization of Biological Networks." ACM Transactions on Embedded Computing Systems 20, no. 4 (2021): 1–20. http://dx.doi.org/10.1145/3427091.

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Model development and simulation of biological networks is recognized as a key task in Systems Biology. Integrated with in vitro and in vivo experimental data, network simulation allows for the discovery of the dynamics that regulate biological systems. Stochastic Petri Nets (SPNs) have become a widespread and reference formalism to model metabolic networks thanks to their natural expressiveness to represent metabolites, reactions, molecule interactions, and simulation randomness due to system fluctuations and environmental noise. In the literature, starting from the network model and the comp

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11

KIRKILIONIS, MARKUS, and LUCA SBANO. "AN AVERAGING PRINCIPLE FOR COMBINED INTERACTION GRAPHS — CONNECTIVITY AND APPLICATIONS TO GENETIC SWITCHES." Advances in Complex Systems 13, no. 03 (2010): 293–326. http://dx.doi.org/10.1142/s0219525910002669.

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Time-continuous dynamical systems defined on graphs are often used to model complex systems with many interacting components in a non-spatial context. In the reverse sense attaching meaningful dynamics to given "interaction diagrams" is a central bottleneck problem in many application areas, especially in cell biology where various such diagrams with different conventions describing molecular regulation are presently in use. In most situations these diagrams can only be interpreted by the use of both discrete and continuous variables during the modelling process, corresponding to both determin

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12

Stutz, Timothy C., Alfonso Landeros, Jason Xu, Janet S. Sinsheimer, Mary Sehl, and Kenneth Lange. "Stochastic simulation algorithms for Interacting Particle Systems." PLOS ONE 16, no. 3 (2021): e0247046. http://dx.doi.org/10.1371/journal.pone.0247046.

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Interacting Particle Systems (IPSs) are used to model spatio-temporal stochastic systems in many disparate areas of science. We design an algorithmic framework that reduces IPS simulation to simulation of well-mixed Chemical Reaction Networks (CRNs). This framework minimizes the number of associated reaction channels and decouples the computational cost of the simulations from the size of the lattice. Decoupling allows our software to make use of a wide class of techniques typically reserved for well-mixed CRNs. We implement the direct stochastic simulation algorithm in the open source program

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13

Antonacci, Yuri, Laura Astolfi, Giandomenico Nollo, and Luca Faes. "Information Transfer in Linear Multivariate Processes Assessed through Penalized Regression Techniques: Validation and Application to Physiological Networks." Entropy 22, no. 7 (2020): 732. http://dx.doi.org/10.3390/e22070732.

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The framework of information dynamics allows the dissection of the information processed in a network of multiple interacting dynamical systems into meaningful elements of computation that quantify the information generated in a target system, stored in it, transferred to it from one or more source systems, and modified in a synergistic or redundant way. The concepts of information transfer and modification have been recently formulated in the context of linear parametric modeling of vector stochastic processes, linking them to the notion of Granger causality and providing efficient tools for

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14

Бурыкин, Yu Burykin, Даянова, et al. "Compartment-cluster modeling of uncertainties according to determinism." Complexity. Mind. Postnonclassic 3, no. 2 (2014): 68–80. http://dx.doi.org/10.12737/5520.

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Transition from determinism to stochastic sand further to chaos (self-organization) in the study of biomechanical systems leads to the problem of chaotic dynamics modeling of a post- ural tremor. In general, there is a problem of identifying the voluntary human movements. In other words biophysics of complex systems has approached the global challenges of voluntary and involuntary performance of any motor functions. The possibility of modeling these processes qualitatively and quantitativelyisdiscussed. Specific models demonstrate the effectiveness of the compartment-cluster modeling of biosys

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15

Cocucci, Tadeo Javier, Manuel Pulido, Juan Pablo Aparicio, Juan Ruíz, Mario Ignacio Simoy, and Santiago Rosa. "Inference in epidemiological agent-based models using ensemble-based data assimilation." PLOS ONE 17, no. 3 (2022): e0264892. http://dx.doi.org/10.1371/journal.pone.0264892.

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To represent the complex individual interactions in the dynamics of disease spread informed by data, the coupling of an epidemiological agent-based model with the ensemble Kalman filter is proposed. The statistical inference of the propagation of a disease by means of ensemble-based data assimilation systems has been studied in previous works. The models used are mostly compartmental models representing the mean field evolution through ordinary differential equations. These techniques allow to monitor the propagation of the infections from data and to estimate several parameters of epidemiolog

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16

Antonacci, Yuri, Ludovico Minati, Luca Faes, et al. "Estimation of Granger causality through Artificial Neural Networks: applications to physiological systems and chaotic electronic oscillators." PeerJ Computer Science 7 (May 18, 2021): e429. http://dx.doi.org/10.7717/peerj-cs.429.

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One of the most challenging problems in the study of complex dynamical systems is to find the statistical interdependencies among the system components. Granger causality (GC) represents one of the most employed approaches, based on modeling the system dynamics with a linear vector autoregressive (VAR) model and on evaluating the information flow between two processes in terms of prediction error variances. In its most advanced setting, GC analysis is performed through a state-space (SS) representation of the VAR model that allows to compute both conditional and unconditional forms of GC by so

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17

Choi, Boseung, Yu-Yu Cheng, Selahattin Cinar, et al. "Bayesian inference of distributed time delay in transcriptional and translational regulation." Bioinformatics 36, no. 2 (2019): 586–93. http://dx.doi.org/10.1093/bioinformatics/btz574.

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Abstract Motivation Advances in experimental and imaging techniques have allowed for unprecedented insights into the dynamical processes within individual cells. However, many facets of intracellular dynamics remain hidden, or can be measured only indirectly. This makes it challenging to reconstruct the regulatory networks that govern the biochemical processes underlying various cell functions. Current estimation techniques for inferring reaction rates frequently rely on marginalization over unobserved processes and states. Even in simple systems this approach can be computationally challengin

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18

Grace, Adam W., Dirk P. Kroese, and Werner Sandmann. "Automated State-Dependent Importance Sampling for Markov Jump Processes via Sampling from the Zero-Variance Distribution." Journal of Applied Probability 51, no. 3 (2014): 741–55. http://dx.doi.org/10.1239/jap/1409932671.

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Many complex systems can be modeled via Markov jump processes. Applications include chemical reactions, population dynamics, and telecommunication networks. Rare-event estimation for such models can be difficult and is often computationally expensive, because typically many (or very long) paths of the Markov jump process need to be simulated in order to observe the rare event. We present a state-dependent importance sampling approach to this problem that is adaptive and uses Markov chain Monte Carlo to sample from the zero-variance importance sampling distribution. The method is applicable to

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Grace, Adam W., Dirk P. Kroese, and Werner Sandmann. "Automated State-Dependent Importance Sampling for Markov Jump Processes via Sampling from the Zero-Variance Distribution." Journal of Applied Probability 51, no. 03 (2014): 741–55. http://dx.doi.org/10.1017/s0021900200011645.

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Many complex systems can be modeled via Markov jump processes. Applications include chemical reactions, population dynamics, and telecommunication networks. Rare-event estimation for such models can be difficult and is often computationally expensive, because typically many (or very long) paths of the Markov jump process need to be simulated in order to observe the rare event. We present a state-dependent importance sampling approach to this problem that is adaptive and uses Markov chain Monte Carlo to sample from the zero-variance importance sampling distribution. The method is applicable to

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20

Karagiannis, Georgios A., and Athanasios D. Panagopoulos. "Dynamic Lognormal Shadowing Framework for the Performance Evaluation of Next Generation Cellular Systems." Future Internet 11, no. 5 (2019): 106. http://dx.doi.org/10.3390/fi11050106.

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Performance evaluation tools for wireless cellular systems are very important for the establishment and testing of future internet applications. As the complexity of wireless networks keeps growing, wireless connectivity becomes the most critical requirement in a variety of applications (considered also complex and unfavorable from propagation point of view environments and paradigms). Nowadays, with the upcoming 5G cellular networks the development of realistic and more accurate channel model frameworks has become more important since new frequency bands are used and new architectures are emp

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21

Read, Mark, Paul S. Andrews, Jon Timmis, and Vipin Kumar. "Modelling biological behaviours with the unified modelling language: an immunological case study and critique." Journal of The Royal Society Interface 11, no. 99 (2014): 20140704. http://dx.doi.org/10.1098/rsif.2014.0704.

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We present a framework to assist the diagrammatic modelling of complex biological systems using the unified modelling language (UML). The framework comprises three levels of modelling, ranging in scope from the dynamics of individual model entities to system-level emergent properties. By way of an immunological case study of the mouse disease experimental autoimmune encephalomyelitis, we show how the framework can be used to produce models that capture and communicate the biological system, detailing how biological entities, interactions and behaviours lead to higher-level emergent properties

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22

Alameddine, Abdallah K., Frederick Conlin, and Brian Binnall. "An Introduction to the Mathematical Modeling in the Study of Cancer Systems Biology." Cancer Informatics 17 (January 2018): 117693511879975. http://dx.doi.org/10.1177/1176935118799754.

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Background: Frequently occurring in cancer are the aberrant alterations of regulatory onco-metabolites, various oncogenes/epigenetic stochasticity, and suppressor genes, as well as the deficient mismatch repair mechanism, chronic inflammation, or those deviations belonging to the other cancer characteristics. How these aberrations that evolve overtime determine the global phenotype of malignant tumors remains to be completely understood. Dynamic analysis may have potential to reveal the mechanism of carcinogenesis and can offer new therapeutic intervention. Aims: We introduce simplified mathem

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23

LIN, CHENG-JIAN. "A FUZZY ADAPTIVE LEARNING CONTROL NETWORK WITH ON-LINE STRUCTURE AND PARAMETER LEARNING." International Journal of Neural Systems 07, no. 05 (1996): 569–90. http://dx.doi.org/10.1142/s0129065796000567.

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This paper addresses a general connectionist model, called Fuzzy Adaptive Learning Control Network (FALCON), for the realization of a fuzzy logic control system. An on-line supervised structure/parameter learning algorithm is proposed for constructing the FALCON dynamically. It combines the backpropagation learning scheme for parameter learning and the fuzzy ART algorithm for structure learning. The supervised learning algorithm has some important features. First of all, it partitions the input state space and output control space using irregular fuzzy hyperboxes according to the distribution

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Pinotti, Francesco, Fakhteh Ghanbarnejad, Philipp Hövel, and Chiara Poletto. "Interplay between competitive and cooperative interactions in a three-player pathogen system." Royal Society Open Science 7, no. 1 (2020): 190305. http://dx.doi.org/10.1098/rsos.190305.

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In ecological systems, heterogeneous interactions between pathogens take place simultaneously. This occurs, for instance, when two pathogens cooperate, while at the same time, multiple strains of these pathogens co-circulate and compete. Notable examples include the cooperation of human immunodeficiency virus with antibiotic-resistant and susceptible strains of tuberculosis or some respiratory infections with Streptococcus pneumoniae strains. Models focusing on competition or cooperation separately fail to describe how these concurrent interactions shape the epidemiology of such diseases. We s

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Gupta, Abhinav, and Pierre F. J. Lermusiaux. "Neural closure models for dynamical systems." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 477, no. 2252 (2021): 20201004. http://dx.doi.org/10.1098/rspa.2020.1004.

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Complex dynamical systems are used for predictions in many domains. Because of computational costs, models are truncated, coarsened or aggregated. As the neglected and unresolved terms become important, the utility of model predictions diminishes. We develop a novel, versatile and rigorous methodology to learn non-Markovian closure parametrizations for known-physics/low-fidelity models using data from high-fidelity simulations. The new neural closure models augment low-fidelity models with neural delay differential equations (nDDEs), motivated by the Mori–Zwanzig formulation and the inherent d

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26

House, Thomas, and Matt J. Keeling. "Insights from unifying modern approximations to infections on networks." Journal of The Royal Society Interface 8, no. 54 (2010): 67–73. http://dx.doi.org/10.1098/rsif.2010.0179.

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Networks are increasingly central to modern science owing to their ability to conceptualize multiple interacting components of a complex system. As a specific example of this, understanding the implications of contact network structure for the transmission of infectious diseases remains a key issue in epidemiology. Three broad approaches to this problem exist: explicit simulation; derivation of exact results for special networks; and dynamical approximations. This paper focuses on the last of these approaches, and makes two main contributions. Firstly, formal mathematical links are demonstrate

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Wu, Xu, Guo-Ping Jiang, and Xinwei Wang. "A New Model for Complex Dynamical Networks Considering Random Data Loss." Entropy 21, no. 8 (2019): 797. http://dx.doi.org/10.3390/e21080797.

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Model construction is a very fundamental and important issue in the field of complex dynamical networks. With the state-coupling complex dynamical network model proposed, many kinds of complex dynamical network models were introduced by considering various practical situations. In this paper, aiming at the data loss which may take place in the communication between any pair of directly connected nodes in a complex dynamical network, we propose a new discrete-time complex dynamical network model by constructing an auxiliary observer and choosing the observer states to compensate for the lost st

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Uthamacumaran, A. "A Review of Complex Systems Approaches to Cancer Networks." Complex Systems 29, no. 4 (2020): 779–835. http://dx.doi.org/10.25088/complexsystems.29.4.779.

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Cancers remain the leading cause of disease-related pediatric death in North America. The emerging field of complex systems has redefined cancer networks as a computational system. Herein, a tumor and its heterogeneous phenotypes are discussed as dynamical systems having multiple strange attractors. Machine learning, network science and algorithmic information dynamics are discussed as current tools for cancer network reconstruction. Deep learning architectures and computational fluid models are proposed for better forecasting gene expression patterns in cancer ecosystems. Cancer cell decision

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Rahimi-Majd, M., J. G. Restrepo, and M. N. Najafi. "Stochastic and deterministic dynamics in networks with excitable nodes." Chaos: An Interdisciplinary Journal of Nonlinear Science 33, no. 2 (2023): 023134. http://dx.doi.org/10.1063/5.0103806.

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Networks of excitable systems provide a flexible and tractable model for various phenomena in biology, social sciences, and physics. A large class of such models undergo a continuous phase transition as the excitability of the nodes is increased. However, models of excitability that result in this continuous phase transition are based implicitly on the assumption that the probability that a node gets excited, its transfer function, is linear for small inputs. In this paper, we consider the effect of cooperative excitations, and more generally the case of a nonlinear transfer function, on the c

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Alekseeva, I. V., O. I. Lysenko, and O. M. Tachinina. "Necessary optimality conditions of control of stochastic compound dynamic system in case of full in-formation about state vector." Mathematical machines and systems 4 (2020): 136–47. http://dx.doi.org/10.34121/1028-9763-2020-4-136-147.

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Stochastic Compound Dynamic Systems (CDS) are complex technical systems that are created through the use of precision mechanics in combination with modern telecommunications and computer technologies. Incertitude in these CDS shows up under the influence of external and internal stochastic perturbations. The constituent elements of CDS are combined into a single system because these elements perform a single complex mission. The information exchange is wireless, there is no mechanical connection between the elements of the CDS. The paper considers groups of unmanned aerial vehicles (UAVs), whi

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Arellano-Delgado, A., C. Cruz-Hernández, R. M. López Gutiérrez, and C. Posadas-Castillo. "Outer Synchronization of Simple Firefly Discrete Models in Coupled Networks." Mathematical Problems in Engineering 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/895379.

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Synchronization is one of the most important emerging collective behaviors in nature, which results from the interaction in groups of organisms. In this paper, network synchronization of discrete-time dynamical systems is studied. In particular, network synchronization with fireflies oscillators like nodes is achieved by using complex systems theory. Different cases of interest on network synchronization are studied, including for a large number of fireflies oscillators; we consider synchronization in small-world networks and outer synchronization among different coupled networks topologies; f

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MUEZZINOGLU, MEHMET K., IRMA TRISTAN, RAMON HUERTA, VALENTIN S. AFRAIMOVICH, and MIKHAIL I. RABINOVICH. "TRANSIENTS VERSUS ATTRACTORS IN COMPLEX NETWORKS." International Journal of Bifurcation and Chaos 20, no. 06 (2010): 1653–75. http://dx.doi.org/10.1142/s0218127410026745.

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Understanding and predicting the behavior of complex multiagent systems like brain or ecological food net requires new approaches and paradigms. Traditional analyses based on just asymptotic results of behavior as time goes to infinity, or on straightforward mathematical images that can accommodate only fixed points or limit cycles do not tell much about these systems. To obtain sensible dynamical models of natural phenomena, such as the reproducible order observed in ecological, cognitive or behavioral experiments, one cannot afford to neglect the transient dynamics of the underlying complex

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Wittenstein, Timon, Nava Leibovich, and Andreas Hilfinger. "Quantifying biochemical reaction rates from static population variability within incompletely observed complex networks." PLOS Computational Biology 18, no. 6 (2022): e1010183. http://dx.doi.org/10.1371/journal.pcbi.1010183.

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Quantifying biochemical reaction rates within complex cellular processes remains a key challenge of systems biology even as high-throughput single-cell data have become available to characterize snapshots of population variability. That is because complex systems with stochastic and non-linear interactions are difficult to analyze when not all components can be observed simultaneously and systems cannot be followed over time. Instead of using descriptive statistical models, we show that incompletely specified mechanistic models can be used to translate qualitative knowledge of interactions int

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Tanzi, Matteo, and Lai-Sang Young. "Existence of physical measures in some excitation–inhibition networks*." Nonlinearity 35, no. 2 (2021): 889–915. http://dx.doi.org/10.1088/1361-6544/ac3eb6.

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Abstract In this paper we present a rigorous analysis of a class of coupled dynamical systems in which two distinct types of components, one excitatory and the other inhibitory, interact with one another. These network models are finite in size but can be arbitrarily large. They are inspired by real biological networks, and possess features that are idealizations of those in biological systems. Individual components of the network are represented by simple, much studied dynamical systems. Complex dynamical patterns on the network level emerge as a result of the coupling among its constituent s

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Xinghua, Hu, and Zhang Yu. "A Parameters Calibration Method in Simulated Complex Traffic Network." Open Construction and Building Technology Journal 9, no. 1 (2015): 262–65. http://dx.doi.org/10.2174/1874836801509010262.

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Traffic simulation models have been extensively used because of their ability to model the dynamic stochastic nature of transportation systems. Parameter calibration is very complex and does not give optimal results easily. Besides, it is also time-consuming especially for large and complex networks. Initially, the procedure of traffic micro-simulation parameter calibration was put forward. A Vehicle Intelligent Simulation Software Model (VISSIM) models were selected for parameter calibration in complex-network, and, the role of Simultaneous Perturbation Genetic Algorithm (SPGA) was examined i

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Halevi, Y., and A. Ray. "Performance Analysis of Integrated Communication and Control System Networks." Journal of Dynamic Systems, Measurement, and Control 112, no. 3 (1990): 365–71. http://dx.doi.org/10.1115/1.2896153.

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This paper presents statistical analysis of delays in Integrated Communication and Control System (ICCS) networks [1–4] that are based on asynchronous time-division multiplexing. The models are obtained in closed form for analyzing control systems with randomly varying delays. The results of this research are applicable to ICCS design for complex dynamical processes like advanced aircraft and spacecraft, autonomous manufacturing plants, and chemical and processing plants.

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ROSSELLÓ, JOSEP L., VINCENT CANALS, ANTONI MORRO, and ANTONI OLIVER. "HARDWARE IMPLEMENTATION OF STOCHASTIC SPIKING NEURAL NETWORKS." International Journal of Neural Systems 22, no. 04 (2012): 1250014. http://dx.doi.org/10.1142/s0129065712500141.

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Spiking Neural Networks, the last generation of Artificial Neural Networks, are characterized by its bio-inspired nature and by a higher computational capacity with respect to other neural models. In real biological neurons, stochastic processes represent an important mechanism of neural behavior and are responsible of its special arithmetic capabilities. In this work we present a simple hardware implementation of spiking neurons that considers this probabilistic nature. The advantage of the proposed implementation is that it is fully digital and therefore can be massively implemented in Field

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Asllani, Malbor, Renaud Lambiotte, and Timoteo Carletti. "Structure and dynamical behavior of non-normal networks." Science Advances 4, no. 12 (2018): eaau9403. http://dx.doi.org/10.1126/sciadv.aau9403.

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We analyze a collection of empirical networks in a wide spectrum of disciplines and show that strong non-normality is ubiquitous in network science. Dynamical processes evolving on non-normal networks exhibit a peculiar behavior, as initial small disturbances may undergo a transient phase and be strongly amplified in linearly stable systems. In addition, eigenvalues may become extremely sensible to noise and have a diminished physical meaning. We identify structural properties of networks that are associated with non-normality and propose simple models to generate networks with a tunable level

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39

Niu, Haoyu, YangQuan Chen, and Bruce J. West. "Why Do Big Data and Machine Learning Entail the Fractional Dynamics?" Entropy 23, no. 3 (2021): 297. http://dx.doi.org/10.3390/e23030297.

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Fractional-order calculus is about the differentiation and integration of non-integer orders. Fractional calculus (FC) is based on fractional-order thinking (FOT) and has been shown to help us to understand complex systems better, improve the processing of complex signals, enhance the control of complex systems, increase the performance of optimization, and even extend the enabling of the potential for creativity. In this article, the authors discuss the fractional dynamics, FOT and rich fractional stochastic models. First, the use of fractional dynamics in big data analytics for quantifying b

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40

Kolářová, Edita. "Applications of second order stochastic integral equations to electrical networks." Tatra Mountains Mathematical Publications 63, no. 1 (2015): 163–73. http://dx.doi.org/10.1515/tmmp-2015-0028.

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The theory of stochastic differential equations is used in various fields of science and engineering. This paper deals with vector-valued stochastic integral equations. We show some applications of the presented theory to the problem of modelling RLC electrical circuits by noisy parameters. From practical point of view, the second-order RLC circuits are of major importance, as they are the building blocks of more complex physical systems. The mathematical models of such circuits lead to the second order differential equations. We construct stochastic models of the RLC circuit by replacing a co

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41

Nikolic, Sasa S., Dragan S. Antic, Nikola B. Dankovic, et al. "Generalized Quasi-Orthogonal Functional Networks Applied in Parameter Sensitivity Analysis of Complex Dynamical Systems." Elektronika ir Elektrotechnika 28, no. 4 (2022): 19–26. http://dx.doi.org/10.5755/j02.eie.31110.

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This paper presents one possible application of generalized quasi-orthogonal functional networks in the sensitivity analysis of complex dynamical systems. First, a new type of first order (k = 1) generalized quasi-orthogonal polynomials of Legendre type via classical quasi-orthogonal polynomials was introduced. The short principle to design generalized quasi-orthogonal polynomials and filters was also shown. A generalized quasi-orthogonal functional network represents an extension of classical orthogonal functional networks and neural networks, which deal with general functional models. A sequ

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42

PLESA, TOMISLAV, RADEK ERBAN, and HANS G. OTHMER. "Noise-induced mixing and multimodality in reaction networks." European Journal of Applied Mathematics 30, no. 5 (2018): 887–911. http://dx.doi.org/10.1017/s0956792518000517.

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We analyse a class of chemical reaction networks under mass-action kinetics involving multiple time scales, whose deterministic and stochastic models display qualitative differences. The networks are inspired by gene-regulatory networks and consist of a slow subnetwork, describing conversions among the different gene states, and fast subnetworks, describing biochemical interactions involving the gene products. We show that the long-term dynamics of such networks can consist of a unique attractor at the deterministic level (unistability), while the long-term probability distribution at the stoc

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43

Bone, Rebecca A., and Jason R. Green. "Optimizing dynamical functions for speed with stochastic paths." Journal of Chemical Physics 157, no. 22 (2022): 224101. http://dx.doi.org/10.1063/5.0125479.

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Living systems are built from microscopic components that function dynamically; they generate work with molecular motors, assemble and disassemble structures such as microtubules, keep time with circadian clocks, and catalyze the replication of DNA. How do we implement these functions in synthetic nanostructured materials to execute them before the onset of dissipative losses? Answering this question requires a quantitative understanding of when we can improve performance and speed while minimizing the dissipative losses associated with operating in a fluctuating environment. Here, we show tha

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44

Abrevaya, Germán, Guillaume Dumas, Aleksandr Y. Aravkin, et al. "Learning Brain Dynamics With Coupled Low-Dimensional Nonlinear Oscillators and Deep Recurrent Networks." Neural Computation 33, no. 8 (2021): 2087–127. http://dx.doi.org/10.1162/neco_a_01401.

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Many natural systems, especially biological ones, exhibit complex multivariate nonlinear dynamical behaviors that can be hard to capture by linear autoregressive models. On the other hand, generic nonlinear models such as deep recurrent neural networks often require large amounts of training data, not always available in domains such as brain imaging; also, they often lack interpretability. Domain knowledge about the types of dynamics typically observed in such systems, such as a certain type of dynamical systems models, could complement purely data-driven techniques by providing a good prior.

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45

Hu, Yiwen, and Markus J. Buehler. "Deep language models for interpretative and predictive materials science." APL Machine Learning 1, no. 1 (2023): 010901. http://dx.doi.org/10.1063/5.0134317.

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Machine learning (ML) has emerged as an indispensable methodology to describe, discover, and predict complex physical phenomena that efficiently help us learn underlying functional rules, especially in cases when conventional modeling approaches cannot be applied. While conventional feedforward neural networks are typically limited to performing tasks related to static patterns in data, recursive models can both work iteratively based on a changing input and discover complex dynamical relationships in the data. Deep language models can model flexible modalities of data and are capable of learn

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46

Ghahramani, Zoubin, and Geoffrey E. Hinton. "Variational Learning for Switching State-Space Models." Neural Computation 12, no. 4 (2000): 831–64. http://dx.doi.org/10.1162/089976600300015619.

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We introduce a new statistical model for time series that iteratively segments data into regimes with approximately linear dynamics and learns the parameters of each of these linear regimes. This model combines and generalizes two of the most widely used stochastic time-series models—hidden Markov models and linear dynamical systems—and is closely related to models that are widely used in the control and econometrics literatures. It can also be derived by extending the mixture of experts neural network (Jacobs, Jordan, Nowlan, & Hinton, 1991) to its fully dynamical version, in which both e

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ANDREYEV, YU V., A. S. DMITRIEV, D. A. KUMINOV, L. O. CHUA, and C. W. WU. "1-D MAPS, CHAOS AND NEURAL NETWORKS FOR INFORMATION PROCESSING." International Journal of Bifurcation and Chaos 06, no. 04 (1996): 627–46. http://dx.doi.org/10.1142/s021812749600031x.

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An application of complex dynamics and chaos in neural networks to information processing is studied. Mathematical models based on piecewise-linear maps implementing basic functions of information processing via complex dynamics and chaos are discussed. Realizations of these models by neural networks are presented. In contrast to other methods of using neural networks and associative memory to store information, the information is stored in dynamical attractors such as limit cycles, rather than equilibrium points. Retrieval of information corresponds to getting the state into the basin of attr

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Hasani, Ramin, Mathias Lechner, Alexander Amini, et al. "Closed-form continuous-time neural networks." Nature Machine Intelligence 4, no. 11 (2022): 992–1003. http://dx.doi.org/10.1038/s42256-022-00556-7.

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AbstractContinuous-time neural networks are a class of machine learning systems that can tackle representation learning on spatiotemporal decision-making tasks. These models are typically represented by continuous differential equations. However, their expressive power when they are deployed on computers is bottlenecked by numerical differential equation solvers. This limitation has notably slowed down the scaling and understanding of numerous natural physical phenomena such as the dynamics of nervous systems. Ideally, we would circumvent this bottleneck by solving the given dynamical system i

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Hanel, Rudolf, Manfred Pöchacker, and Stefan Thurner. "Living on the edge of chaos: minimally nonlinear models of genetic regulatory dynamics." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 368, no. 1933 (2010): 5583–96. http://dx.doi.org/10.1098/rsta.2010.0267.

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Linearized catalytic reaction equations (modelling, for example, the dynamics of genetic regulatory networks), under the constraint that expression levels, i.e. molecular concentrations of nucleic material, are positive, exhibit non-trivial dynamical properties, which depend on the average connectivity of the reaction network. In these systems, an inflation of the edge of chaos and multi-stability have been demonstrated to exist. The positivity constraint introduces a nonlinearity, which makes chaotic dynamics possible. Despite the simplicity of such minimally nonlinear systems , their basic p

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WANG, HONGCHUN, KEQING HE, BING LI, and JINHU LÜ. "ON SOME RECENT ADVANCES IN COMPLEX SOFTWARE NETWORKS: MODELING, ANALYSIS, EVOLUTION AND APPLICATIONS." International Journal of Bifurcation and Chaos 22, no. 02 (2012): 1250024. http://dx.doi.org/10.1142/s0218127412500241.

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Complex software networks, as a typical kind of man-made complex networks, have attracted more and more attention from various fields of science and engineering over the past ten years. With the dramatic increase of scale and complexity of software systems, it is essential to develop a systematic approach to further investigate the complex software systems by using the theories and methods of complex networks and complex adaptive systems. This paper attempts to briefly review some recent advances in complex software networks and also develop some novel tools to further analyze complex software

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