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Статті в журналах з теми "Complex systems, networks, dynamical models on networks, stochastic models"
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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 (July 2021): eabf8124. http://dx.doi.org/10.1126/sciadv.abf8124.
Повний текст джерелаАнотація:
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-standing open question of how attractor count in critical random Boolean networks scales with network size and whether the scaling matches biological observations. Via 80-fold improvement in probed network size (N = 16,384), we find the unexpectedly low scaling exponent of 0.12 ± 0.05, approximately one-tenth the analytical upper bound. We demonstrate a general principle: A system’s relationship to its time reversal and state-space inversion constrains its repertoire of emergent behaviors.
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 (December 2022): 123102. http://dx.doi.org/10.1063/5.0122184.
Повний текст джерелаАнотація:
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 and can be controlled by tunable parameters. To test our model’s ability to simulate realistic biological phenomena, we turned to one of the most studied organisms, C. elegans, well known for its limited behavioral states. We reconstructed experimental data from two laboratories, demonstrating the model’s ability to quantitatively reproduce dwell times and transition statistics under a variety of conditions. Stochastic switching between dominant states in complex dynamical systems has been extensively studied and is often modeled as Markov chains. As an alternative, we propose here a new paradigm, namely, chaotic heteroclinic networks generated by deterministic rules (without the necessity for noise). Chaotic heteroclinic networks can be used to model systems with arbitrary architecture and size without a commensurate increase in phase dimension. They are highly flexible and able to capture a wide range of transition characteristics that can be adjusted through control parameters.
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 (February 28, 2022): 015010. http://dx.doi.org/10.1088/2632-072x/ac52e6.
Повний текст джерелаАнотація:
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 parameters capturing structural properties, as reciprocity for instance. Remarkably, these models are capable of generating more realistic network representations than those that only consider community membership. To this aim, we present a probabilistic generative model with hidden variables that integrates reciprocity and communities as structural information of networks that evolve in time. The model assumes a fundamental order in observing reciprocal data, that is an edge is observed, conditional on its reciprocated edge in the past. We deploy a Markovian approach to construct the network’s transition matrix between time steps and parameters’ inference is performed with an expectation-maximization algorithm that leads to high computational efficiency because it exploits the sparsity of the dataset. We test the performance of the model on synthetic dynamical networks, as well as on real networks of citations and email datasets. We show that our model captures the reciprocity of real networks better than standard models with only community structure, while performing well at link prediction tasks.
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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 (December 12, 2013): 405–26. http://dx.doi.org/10.1142/s0218202513400137.
Повний текст джерелаАнотація:
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 systems. An application on opinion formation follows to show how the theory can generate specific models.
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Jirsa, Viktor, and Hiba Sheheitli. "Entropy, free energy, symmetry and dynamics in the brain." Journal of Physics: Complexity 3, no. 1 (February 3, 2022): 015007. http://dx.doi.org/10.1088/2632-072x/ac4bec.
Повний текст джерелаАнотація:
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 information entropy and free energy to mechanisms of emergent dynamics and self-organization in brain networks. We identify basic properties of neuronal populations leading to an equivariant matrix in a network, in which complex behaviors can naturally be represented through structured flows on manifolds establishing the internal model relevant to theories of brain function. We propose a neural mechanism for the generation of internal models from symmetry breaking in the connectivity of brain networks. The emergent perspective illustrates how free energy can be linked to internal models and how they arise from the neural substrate.
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Penfold, Christopher A., and David L. Wild. "How to infer gene networks from expression profiles, revisited." Interface Focus 1, no. 6 (August 10, 2011): 857–70. http://dx.doi.org/10.1098/rsfs.2011.0053.
Повний текст джерелаАнотація:
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 datasets. Here, we revisit this work by assessing the performance of more recent network inference algorithms, including a novel non-parametric learning approach based upon nonlinear dynamical systems. For larger GRNs, containing hundreds of genes, these non-parametric approaches more accurately infer network structures than do traditional approaches, but at significant computational cost. For smaller systems, DBNs are competitive with the non-parametric approaches with respect to computational time and accuracy, and both of these approaches appear to be more accurate than Granger causality-based methods and those using simple ODEs models.
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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 (December 19, 2005): 483–93. http://dx.doi.org/10.1098/rsif.2005.0105.
Повний текст джерелаАнотація:
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 network models, we consider here the special case where the dynamics of the network topology change on time-scales much longer than the epidemiological processes imposed on them; in such cases, the use of static network models are justified. We show that in such cases, static network models may provide excellent approximations to the underlying spatial contact process through an appropriate choice of the effective neighbourhood size. We also demonstrate the robustness of this mapping by examining the equivalence of deterministic approximations to the full spatial and network models derived under third-order moment closure assumptions. For systems where deviation from homogeneous mixing is limited, we show that pair equations developed for network models are at least as good an approximation to the underlying stochastic spatial model as more complex spatial moment equations, with both classes of approximation becoming less accurate only for highly localized kernels.
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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 (February 2019): 20180943. http://dx.doi.org/10.1098/rsif.2018.0943.
Повний текст джерелаАнотація:
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 and variability of a stochastic biochemical reaction network requires many probabilistic simulations of its evolution. Using a biochemical reaction network model to assist in the interpretation of time-course data from a biological experiment is an even greater challenge due to the intractability of the likelihood function for determining observation probabilities. These computational challenges have been subjects of active research for over four decades. In this review, we present an accessible discussion of the major historical developments and state-of-the-art computational techniques relevant to simulation and inference problems for stochastic biochemical reaction network models. Detailed algorithms for particularly important methods are described and complemented with Matlab ® implementations. As a result, this review provides a practical and accessible introduction to computational methods for stochastic models within the life sciences community.
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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 (February 3, 2021): 2175–82. http://dx.doi.org/10.1093/bioinformatics/btab081.
Повний текст джерелаАнотація:
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 relation between the species, resulting in a reduced network where the dynamics of each macro-species is stochastically equivalent to the sum of the original species in each equivalence class, for any choice of the initial state of the system. Furthermore, by an appropriate encoding of kinetic parameters as additional species, the method can establish equivalences that do not depend on specific values of the parameters. The method is supported by an efficient algorithm to compute the largest species equivalence, thus the maximal lumping. The effectiveness and scalability of our lumping technique, as well as the physical interpretability of resulting reductions, is demonstrated in several models of signaling pathways and epidemic processes on complex networks. Availability and implementation The algorithms for species equivalence have been implemented in the software tool ERODE, freely available for download from https://www.erode.eu. Supplementary information Supplementary data are available at Bioinformatics online.
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Bombieri, Nicola, Silvia Scaffeo, Antonio Mastrandrea, Simone Caligola, Tommaso Carlucci, Franco Fummi, Carlo Laudanna, Gabriela Constantin, and Rosalba Giugno. "SystemC Implementation of Stochastic Petri Nets for Simulation and Parameterization of Biological Networks." ACM Transactions on Embedded Computing Systems 20, no. 4 (June 2021): 1–20. http://dx.doi.org/10.1145/3427091.
Повний текст джерелаАнотація:
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 complete set of system parameters, there exist frameworks that allow for dynamic system simulation. Nevertheless, they do not allow for automatic model parameterization, which is a crucial task to identify, in silico, the network configurations that lead the model to satisfy specific temporal properties. To cover such a gap, this work first presents a framework to implement SPN models into SystemC code. Then, it shows how the framework allows for automatic parameterization of the networks. The user formally defines the network properties to be observed and the framework automatically extrapolates, through Assertion-based Verification (ABV), the parameter configurations that satisfy such properties. We present the results obtained by applying the proposed framework to model the complex metabolic network of the purine metabolism. We show how the automatic extrapolation of the system parameters allowed us to simulate the model under different conditions, which led to the understanding of behavioral differences in the regulation of the entire purine network. We also show the scalability of the approach through the modeling and simulation of four biological networks, each one with different structural characteristics.
Дисертації з теми "Complex systems, networks, dynamical models on networks, stochastic models"
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Preciado, Víctor Manuel. "Spectral analysis for stochastic models of large-scale complex dynamical networks." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/45873.
Повний текст джерелаАнотація:
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.Includes bibliographical references (p. 179-196).
Research on large-scale complex networks has important applications in diverse systems of current interest, including the Internet, the World-Wide Web, social, biological, and chemical networks. The growing availability of massive databases, computing facilities, and reliable data analysis tools has provided a powerful framework to explore structural properties of such real-world networks. However, one cannot efficiently retrieve and store the exact or full topology for many large-scale networks. As an alternative, several stochastic network models have been proposed that attempt to capture essential characteristics of such complex topologies. Network researchers then use these stochastic models to generate topologies similar to the complex network of interest and use these topologies to test, for example, the behavior of dynamical processes in the network. In general, the topological properties of a network are not directly evident in the behavior of dynamical processes running on it. On the other hand, the eigenvalue spectra of certain matricial representations of the network topology do relate quite directly to the behavior of many dynamical processes of interest, such as random walks, Markov processes, virus/rumor spreading, or synchronization of oscillators in a network. This thesis studies spectral properties of popular stochastic network models proposed in recent years. In particular, we develop several methods to determine or estimate the spectral moments of these models. We also present a variety of techniques to extract relevant spectral information from a finite sequence of spectral moments. A range of numerical examples throughout the thesis confirms the efficacy of our approach. Our ultimate objective is to use such results to understand and predict the behavior of dynamical processes taking place in large-scale networks.
by Víctor Manuel Preciado.
Ph.D.
2
Vallès, Català Toni. "Network inference based on stochastic block models: model extensions, inference approaches and applications." Doctoral thesis, Universitat Rovira i Virgili, 2016. http://hdl.handle.net/10803/399539.
Повний текст джерелаАнотація:
L'estudi de xarxes ha contribuït a la comprensió de sistemes complexos en una àmplia gamma de camps com la
biologia molecular i cel·lular, l'anatomia, la neurociència, l'ecologia, l'economia i la sociologia.
No obstant, el coneixement disponible sobre molts sistemes reals encara és limitat, per aquesta raó el poder predictiu
de la ciència en xarxes s'ha de millorar per disminuir la bretxa entre coneixement i informació.
Per abordar aquest tema fem servir la família de 'Stochastic Block Models' (SBM), una família de models generatius
que està guanyant gran interès recentment a causa de la seva adaptabilitat a qualsevol tipus de xarxa.
L'objectiu d'aquesta tesi és el desenvolupament de noves metodologies d'inferència basades en SBM que
perfeccionaran la nostra comprensió de les xarxes complexes.
En primer lloc, investiguem en quina mesura fer un mostreg sobre models pot millorar significativament la capacitat de
predicció que considerar un únic conjunt òptim de paràmetres.
Un cop sabem quin model és capaç de descriure millor una xarxa determinada, apliquem aquest mètode en un cas
particular d'una xarxa real: una xarxa basada en les interaccions/sutures entre els ossos del crani en nounats.
Concretament, descobrim que les sutures tancades a causa d'una malaltia patològica en el nounat humà son menys
probables, des d'un punt de vista morfològic, que les sutures tancades sota un desenvolupament normal.
Recents investigacions en xarxes multicapa conclou que el comportament de les xarxes d'una sola capa són diferents de les de múltiples capes; d'altra banda, les xarxes del món real se'ns presenten com xarxes d'una sola capa.El estudio de las redes del mundo real han empujado hacia la comprensión de sistemas complejos en una amplia gama de campos como la biología molecular y celular, la anatomía, la neurociencia, la ecología, la economía y la sociología . Sin embargo, el conocimiento disponible de muchos sistemas reales aún es limitado, por esta razón el poder predictivo de la ciencia en redes se debe mejorar para disminuir la brecha entre conocimiento y información. Para abordar este tema usamos la familia de 'Stochastic Block Modelos' (SBM), una familia de modelos generativos que está ganando gran interés recientemente debido a su adaptabilidad a cualquier tipo de red. El objetivo de esta tesis es el desarrollo de nuevas metodologías de inferencia basadas en SBM que perfeccionarán nuestra comprensión de las redes complejas. En primer lugar, investigamos en qué medida hacer un muestreo sobre modelos puede mejorar significativamente la capacidad de predicción a considerar un único conjunto óptimo de parámetros. Seguidamente, aplicamos el método mas predictivo en una red real particular: una red basada en las interacciones/suturas entre los huesos del cráneo humano en recién nacidos. Concretamente, descubrimos que las suturas cerradas a causa de una enfermedad patológica en recién nacidos son menos probables, desde un punto de vista morfológico, que las suturas cerradas bajo un desarrollo normal. Concretamente, descubrimos que las suturas cerradas a causa de una enfermedad patológica en recién nacidos son menos probables, desde un punto de vista morfológico, que las suturas cerradas bajo un desarrollo normal. Recientes investigaciones en las redes multicapa concluye que el comportamiento de las redes en una sola capa son diferentes a las de múltiples capas; por otra parte, las redes del mundo real se nos presentan como redes con una sola capa. La parte final de la tesis está dedicada a diseñar un nuevo enfoque en el que dos SBM separados describen simultáneamente una red dada que consta de una sola capa, observamos que esta metodología predice mejor que la metodología de un SBM solo.
The study of real-world networks have pushed towards to the understanding of complex systems in a wide range of fields as molecular and cell biology, anatomy, neuroscience, ecology, economics and sociology. However, the available knowledge from most systems is still limited, hence network science predictive power should be enhanced to diminish the gap between knowledge and information. To address this topic we handle with the family of Stochastic Block Models (SBMs), a family of generative models that are gaining high interest recently due to its adaptability to any kind of network structure. The goal of this thesis is to develop novel SBM based inference approaches that will improve our understanding of complex networks. First, we investigate to what extent sampling over models significatively improves the predictive power than considering an optimal set of parameters alone. Once we know which model is capable to describe better a given network, we apply such method in a particular real world network case: a network based on the interactions/sutures between bones in newborn skulls. Notably, we discovered that sutures fused due to a pathological disease in human newborn were less likely, from a morphological point of view, that those sutures that fused under a normal development. Recent research on multilayer networks has concluded that the behavior of single-layered networks are different from those of multilayer ones; notwhithstanding, real world networks are presented to us as single-layered networks. The last part of the thesis is devoted to design a novel approach where two separate SBMs simultaneously describe a given single-layered network. We importantly find that it predicts better missing/spurious links that the single SBM approach.
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Boutkhamouine, Brahim. "Stochastic modelling of flood phenomena based on the combination of mechanist and systemic approaches." Thesis, Toulouse, INPT, 2018. http://www.theses.fr/2018INPT0142/document.
Повний текст джерелаАнотація:
Les systèmes de prévision des crues décrivent les transformations pluie-débit en se basant sur des représentations simplifiées. Ces représentations modélisent les processus physiques impliqués avec des descriptions empiriques, ou basées sur des équations de la mécanique classique. Les performances des modèles actuels de prévision des crues sont affectées par différentes incertitudes liées aux approximations et aux paramètres du modèle, aux données d’entrée et aux conditions initiales du bassin versant. La connaissance de ces incertitudes permet aux décideurs de mieux interpréter les prévisions et constitue une aide à la décision lors de la gestion de crue. L’analyse d’incertitudes dans les modèles hydrologiques existants repose le plus souvent sur des simulations de Monte-Carlo (MC). La mise en œuvre de ce type de techniques requiert un grand nombre de simulations et donc un temps de calcul potentiellement important. L'estimation des incertitudes liées à la modélisation hydrologique en temps réel reste donc une gageure. Dans ce projet de thèse, nous développons une méthodologie de prévision des crues basée sur les réseaux Bayésiens (RB). Les RBs sont des graphes acycliques dans lesquels les nœuds correspondent aux variables caractéristiques du système modélisé et les arcs représentent les dépendances probabilistes entre ces variables. La méthodologie présentée propose de construire les RBs à partir des principaux facteurs hydrologiques contrôlant la génération des crues, en utilisant à la fois les observations disponibles de la réponse du système et les équations déterministes décrivant les processus concernés. Elle est conçue pour prendre en compte la variabilité temporelle des différentes variables impliquées. Les dépendances probabilistes entre les variables (paramètres) peuvent être spécifiées en utilisant des données observées, des modèles déterministes existants ou des avis d’experts. Grâce à leurs algorithmes d’inférence, les RBs sont capables de propager rapidement, à travers le graphe, différentes sources d'incertitudes pour estimer leurs effets sur la sortie du modèle (ex. débit d'une rivière). Plusieurs cas d’études sont testés. Le premier cas d’étude concerne le bassin versant du Salat au sud-ouest de la France : un RB est utilisé pour simuler le débit de la rivière à une station donnée à partir des observations de 3 stations hydrométriques localisées en amont. Le modèle présente de bonnes performances pour l'estimation du débit à l’exutoire. Utilisé comme méthode inverse, le modèle affiche également de bons résultats quant à la caractérisation de débits d’une station en amont par propagation d’observations de débit sur des stations en aval. Le deuxième cas d’étude concerne le bassin versant de la Sagelva situé en Norvège, pour lequel un RB est utilisé afin de modéliser l'évolution du contenu en eau de la neige en fonction des données météorologiques disponibles. Les performances du modèle sont conditionnées par les données d’apprentissage utilisées pour spécifier les paramètres du modèle. En l'absence de données d'observation pertinentes pour l’apprentissage, une méthodologie est proposée et testée pour estimer les paramètres du RB à partir d’un modèle déterministe. Le RB résultant peut être utilisé pour effectuer des analyses d’incertitudes sans recours aux simulations de Monte-Carlo. Au regard des résultats enregistrés sur les différents cas d’études, les RBs se révèlent utiles et performants pour une utilisation en support d’un processus d'aide à la décision dans le cadre de la gestion du risque de crueFlood forecasting describes the rainfall-runoff transformation using simplified representations. These representations are based on either empirical descriptions, or on equations of classical mechanics of the involved physical processes. The performances of the existing flood predictions are affected by several sources of uncertainties coming not only from the approximations involved but also from imperfect knowledge of input data, initial conditions of the river basin, and model parameters. Quantifying these uncertainties enables the decision maker to better interpret the predictions and constitute a valuable decision-making tool for flood risk management. Uncertainty analysis on existing rainfall-runoff models are often performed using Monte Carlo (MC)- simulations. The implementation of this type of techniques requires a large number of simulations and consequently a potentially important calculation time. Therefore, quantifying uncertainties of real-time hydrological models is challenging. In this project, we develop a methodology for flood prediction based on Bayesian networks (BNs). BNs are directed acyclic graphs where the nodes correspond to the variables characterizing the modelled system and the arcs represent the probabilistic dependencies between these variables. The presented methodology suggests to build the RBs from the main hydrological factors controlling the flood generation, using both the available observations of the system response and the deterministic equations describing the processes involved. It is, thus, designed to take into account the time variability of different involved variables. The conditional probability tables (parameters), can be specified using observed data, existing hydrological models or expert opinion. Thanks to their inference algorithms, BN are able to rapidly propagate, through the graph, different sources of uncertainty in order to estimate their effect on the model output (e.g. riverflow). Several case studies are tested. The first case study is the Salat river basin, located in the south-west of France, where a BN is used to simulate the discharge at a given station from the streamflow observations at 3 hydrometric stations located upstream. The model showed good performances estimating the discharge at the outlet. Used in a reverse way, the model showed also satisfactory results when characterising the discharges at an upstream station by propagating back discharge observations of some downstream stations. The second case study is the Sagelva basin, located in Norway, where a BN is used to simulate the accumulation of snow water equivalent (SWE) given available weather data observations. The performances of the model are affected by the learning dataset used to train the BN parameters. In the absence of relevant observation data for learning, a methodology for learning the BN-parameters from deterministic models is proposed and tested. The resulted BN can be used to perform uncertainty analysis without any MC-simulations to be performed in real-time. From these case studies, it appears that BNs are a relevant decisionsupport tool for flood risk management
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Nicoletti, Sara, Duccio Fanelli, Giorgio Battistelli, Luigi Chisci, and Giacomo Innocenti. "Nonlinear dynamics on networks: deterministic and stochastic approaches." Doctoral thesis, 2021. http://hdl.handle.net/2158/1235962.
Повний текст джерелаАнотація:
Many physical phenomena occurring in nature can be described through
complex systems as collective dynamics which emerge at the macroscopic level
from the interactions of microscopically constituents. Networks dene the struc-
ture of such systems, where the nodes represent the agents interconnected
through intricated patterns of interactions. Neurons activity in the brain,
species interacting in ecosystems, individuals that constitute social systems are
typical examples of systems that can be described as systems hosted on top
of networks and whose evolution is strictly related to the organization of the
underlying support.
Studying dynamical processes on networks represent one of the most inter-
esting application in network science. Noise is a key ingredient in the dynamical
description of many real-world phenomena where stochastic perturbations can
derive from an external noise, due for example to termal
uctuations, or can
stem from an endogeneous noise, re
ecting the inherent discreteness of the scru-
tinized medium.
In diusion or other linear processes, the dynamics can be described by simple
interactions between adjacent nodes, otherwise it could also involve a local reac-
tion on the single nodes. A classical example is represented by reaction-diusion
processes, where the elements interact depending on specic self-reactions and
diuse across the spatial medium dened by the network.
Reaction-diusion processes on networks represent the focus of this thesis. The
evolution of dynamical systems hosted on the single nodes changes inserting the
couplings and unexpected collective behaviors may emerge, like for examples
chaotic systems that synchronize to the same solution, or stationary inhomoge-
neous patterns emerging from diusing species.
The rst dynamical model we present is a simplied version of the cele-
brated Wilson-Cowan model for neurons in the brain and consists in a two-
species model of the excitatory-inhibitory type where the neurons are supposed
to occupy the three nodes located at the vertices of a triangular loop. The phe-
nomenon of stochastic quasicycles is investigated as consequence of the interplay
between endogeneous noise and non-normality of the system. Non-normality
proves a key ingredient to guarantee the amplication eects on noise-assisted
oscillations: when the system is constrained to evolve with a constant rate of
deterministic damping for the perturbations, the amplication correlates with
the degree of non-normal reactivity, a measure that quantify the ability of the
system to prompt an initial perturbation.
We carry on the study of non-normality considering a stochastic reaction-
diusion two-species model on network where the species are assumed to interact
on each node following a non-normal reaction scheme. Replicating the interac-
tion unit on a directed linear lattice, the combined eect of non-normality and
degenerate spectrum of the embedding support guarantee the amplication process of the noise. The same phenomenon takes place when the system is hosted
on a quasi-degenerate network, in the sense that the eigenvalues of the Laplacian
operator accumulate in a compact region of the complex plane. The coopera-
tion of non-normality and quasi-degenerate spectrum may then impact on the
perception of stability, as predicted by conventional deterministic methods, and
modify the usual denition of resilience, the ability of a system to oppose to ex-
ternal perturbations. The concept of resilience is relevant in many elds, from
ecology to climate change, via information security and energy development.
In this respect, the eigenmodes associated to a quasi-degenerate spectrum rep-
resent a route to drive the instability. For this motifs, we perform the rst
generative algorithm of this thesis. The idea is to design a network assembling
nodes in such a way that the associated Laplacian possesses a quasi-degenerate
spectrum. The resulting network is a distorted one-dimensional directed chain
and belongs to the class of the so called directed acyclic graphs.
This opened the way to the subsequent part of our work: network generation.
For reaction-diusion models, dynamics, and the inherent stability, depends on
the Laplacian matrix and its spectrum of eigenvalues. As we mentioned above,
the concept of stability is of paramount importance as it relates to resilience.
It is then crucial to investigate on possible strategies to enforce stability in a
desired system. Based on these observations, we aim at provide a novel math-
ematical procedure to generate networks with any desired Laplacian spectrum.
The obtained networks are weighted and fully connected, so we present two
sparsication procedures to remove unessential links preserving the structure of
the adjacency matrices. In a special working setting, we write the analytical
expressions for the entries of the Laplacian matrix and we consequently derive
conditions to have positive non-diagonal entries for such matrix.
The built sparsied networks are tested simulating two models of oscillators,
the Stuart-Landau and the Kuramoto model.
In the nal part of the thesis we focus on high-order interaction structures.
In many cases of interest, like for instance brain networks, protein interaction
networks, ecological communities and co-authorship networks, the basic inter-
action involves more than two nodes at the same time. Such systems necessi-
tate to be described by a structure accounting for multi-body interactions, like
hypergraphs. In our work, we study dynamical systems dened on top of hyper-
graphs. The Master Stability Function proves essential in our applications to
reaction-diusion systems and synchronization model, studying the stability of
the associated homogeneous equilibria. We show that the role of the localization
property of the Laplace operator is crucial in emerging of collective behavior,
so we investigate on this property aiming at expand this analysis in a more for-
mal way. Localization properties of random networks was already predicted by
the perturbation theory, thus, in a very similar way, we apply the perturbation
theory to the newly Laplacian matrix introduced for hypergraphs.
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(9792245), Ke Ding. "Studying delay effects on complex dynamical networks." Thesis, 2011. https://figshare.com/articles/thesis/Studying_delay_effects_on_complex_dynamical_networks/13456898.
Повний текст джерелаАнотація:
"Complex networks have attracted increasing attention form many fields due to their theoretical importance and practical applications. Because there exist the limitations and constraints for the speeds of transmission, a signal traveling from one node to the other node in a complex network usually suffers a time delay. This thesis is to introduce some complex dynamical network models with coupling delays and to study time delay effects on complex dynamical networks"--Abstract.
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(8086250), Viplove Arora. "A Generalized Framework for Representing Complex Networks." Thesis, 2019.
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Complex systems are often characterized by a large collection of components interacting in nontrivial ways. Self-organization among these individual components often leads to emergence of a macroscopic structure that is neither completely regular nor completely random. In order to understand what we observe at a macroscopic scale, conceptual, mathematical, and computational tools are required for modeling and analyzing these interactions. A principled approach to understand these complex systems (and the processes that give rise to them) is to formulate generative models and infer their parameters from given data that is typically stored in the form of networks (or graphs). The increasing availability of network data from a wide variety of sources, such as the Internet, online social networks, collaboration networks, biological networks, etc., has fueled the rapid development of network science.
A variety of generative models have been designed to synthesize networks having specific properties (such as power law degree distributions, small-worldness, etc.), but the structural richness of real-world network data calls for researchers to posit new models that are capable of keeping pace with the empirical observations about the topological properties of real networks. The mechanistic approach to modeling networks aims to identify putative mechanisms that can explain the dependence, diversity, and heterogeneity in the interactions responsible for creating the topology of an observed network. A successful mechanistic model can highlight the principles by which a network is organized and potentially uncover the mechanisms by which it grows and develops. While it is difficult to intuit appropriate mechanisms for network formation, machine learning and evolutionary algorithms can be used to automatically infer appropriate network generation mechanisms from the observed network structure.
Building on these philosophical foundations and a series of (not new) observations based on first principles, we extrapolate an action-based framework that creates a compact probabilistic model for synthesizing real-world networks. Our action-based perspective assumes that the generative process is composed of two main components: (1) a set of actions that expresses link formation potential using different strategies capturing the collective behavior of nodes, and (2) an algorithmic environment that provides opportunities for nodes to create links. Optimization and machine learning methods are used to learn an appropriate low-dimensional action-based representation for an observed network in the form of a row stochastic matrix, which can subsequently be used for simulating the system at various scales. We also show that in addition to being practically relevant, the proposed model is relatively exchangeable up to relabeling of the node-types.
Such a model can facilitate handling many of the challenges of understanding real data, including accounting for noise and missing values, and connecting theory with data by providing interpretable results. To demonstrate the practicality of the action-based model, we decided to utilize the model within domain-specific contexts. We used the model as a centralized approach for designing resilient supply chain networks while incorporating appropriate constraints, a rare feature of most network models. Similarly, a new variant of the action-based model was used for understanding the relationship between the structural organization of human brains and the cognitive ability of subjects. Finally, our analysis of the ability of state-of-the-art network models to replicate the expected topological variations in network populations highlighted the need for rethinking the way we evaluate the goodness-of-fit of new and existing network models, thus exposing significant gaps in the literature.
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Adam, Ihusan. "Structure and collective behaviour: a focus on the inverse problem." Doctoral thesis, 2021. http://hdl.handle.net/2158/1230776.
Повний текст джерелаАнотація:
The aim of this thesis is to inform our understanding of the exquisite relationship between function and structure of complex systems with a particular focus on the inverse problem of inferring structure from collective expression. There exists a rich body of work explaining complex collective behaviour through its interdependence on structure and this forms the core subject matter of the field complex networks. However, in many cases of interest, the underlying structure of the observed system is often unknown and can only be studied through limited measurements. The first chapters of this thesis develop and refine a method of inferring the structure of a priori unknown networks by leveraging the celebrated Heterogeneous mean-field approximations. The inverse protocol is first formulated for and rigorously challenged against synthetic simulations of reactive-random-walkers to successfully recover the degree distributions from partial observations of the system. The reconstruction framework developed is powerful enough to be applicable to many real-world systems of great interest. This is demonstrated by the extension of the method to a nonlinear Leaky-Integrate and Fire (LIF) excitatory neuronal model evolving on a directed network support to recover both the in-degree distribution and the distribution of associated current in Chapter 5. In this chapter, this method is also applied to wide-field calcium imaging data from the brains of mice undergoing stroke and rehabilitation, which is presented as a spatiotemporal analysis in Chapter 4. The findings of Chapters 4 and 5 complement each other to showcase two potential non-invasive ways of tracking the post-stroke recovery of these animals. One analysis focuses on the subtle changes in propagation patterns quantified through three novel biomarkers, while the other shifts the attention to the changes in structure and inherent dynamics as seen through the inverse protocol. This reconstruction recipe has also been extended to a more general two species LIF model accounting for both inhibitory and excitatory neurons. in Chapter 6. This was applied to two-photon light-sheet microscopy data from zebrafish brains upon successful validation in silico. Lastly, Chapter 7 studies a particular phenomenon of interest where structure and inherent dynamics affect the function in a different but popular class of networks. A zero-mean noise-like prestrain is used to induce contractions in 1D Elastic Network Models. The analysis shows that the exact solution is difficult to probe analytically, while the mean behaviours of the networks are predictable and controllable by tuning the magnitude of the applied prestrain.
Книги з теми "Complex systems, networks, dynamical models on networks, stochastic models"
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Stochastic Chemical Kinetics: Theory and Systems Biological Applications. Springer, 2014.
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Érdi, Péter, and Gábor Lente. Stochastic Chemical Kinetics: Theory and Systems Biological Applications. Springer, 2014.
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Érdi, Péter, and Gábor Lente. Stochastic Chemical Kinetics: Theory and Systems Biological Applications. Springer, 2016.
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4
Koch, Christof. Biophysics of Computation. Oxford University Press, 1998. http://dx.doi.org/10.1093/oso/9780195104912.001.0001.
Повний текст джерелаАнотація:
Neural network research often builds on the fiction that neurons are simple linear threshold units, completely neglecting the highly dynamic and complex nature of synapses, dendrites, and voltage-dependent ionic currents. Biophysics of Computation: Information Processing in Single Neurons challenges this notion, using richly detailed experimental and theoretical findings from cellular biophysics to explain the repertoire of computational functions available to single neurons. The author shows how individual nerve cells can multiply, integrate, or delay synaptic inputs and how information can be encoded in the voltage across the membrane, in the intracellular calcium concentration, or in the timing of individual spikes. Key topics covered include the linear cable equation; cable theory as applied to passive dendritic trees and dendritic spines; chemical and electrical synapses and how to treat them from a computational point of view; nonlinear interactions of synaptic input in passive and active dendritic trees; the Hodgkin-Huxley model of action potential generation and propagation; phase space analysis; linking stochastic ionic channels to membrane-dependent currents; calcium and potassium currents and their role in information processing; the role of diffusion, buffering and binding of calcium, and other messenger systems in information processing and storage; short- and long-term models of synaptic plasticity; simplified models of single cells; stochastic aspects of neuronal firing; the nature of the neuronal code; and unconventional models of sub-cellular computation. Biophysics of Computation: Information Processing in Single Neurons serves as an ideal text for advanced undergraduate and graduate courses in cellular biophysics, computational neuroscience, and neural networks, and will appeal to students and professionals in neuroscience, electrical and computer engineering, and physics.
Частини книг з теми "Complex systems, networks, dynamical models on networks, stochastic models"
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Becker, Till, and Darja Wagner-Kampik. "Complex Networks in Manufacturing and Logistics: A Retrospect." In Dynamics in Logistics, 57–70. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88662-2_3.
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AbstractThe methodology to model systems as graphs or networks already exists for a long time. The availability of information technology and computational power has led to a renaissance of the network modeling approach. Scientists have collected data and started to create huge models of complex networks from various domains. Manufacturing and logistics benefits from this development, because material flow systems are predetermined to be modeled as networks. This chapter revisits selected advances in network modeling and analysis in manufacturing and logistics that have been achieved in the last decade. It presents the basic modeling concept, the transition from static to dynamic and stochastic models, and a collection of examples how network models can be applied to contribute to solving problems in planning and control of logistic systems.
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Wu, Xunxun, Pengfei Jiao, Yaping Wang, Tianpeng Li, Wenjun Wang, and Bo Wang. "Dynamic Stochastic Block Model with Scale-Free Characteristic for Temporal Complex Networks." In Database Systems for Advanced Applications, 502–18. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18579-4_30.
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Lozovanu, Dmitrii, and Stefan Pickl. "A Game-Theoretical Approach to Markov Decision Processes, Stochastic Positional Games and Multicriteria Control Models." In Optimization of Stochastic Discrete Systems and Control on Complex Networks, 213–339. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11833-8_3.
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"Graph Models." In Synchronization in Complex Networks of Nonlinear Dynamical Systems, 31–49. WORLD SCIENTIFIC, 2007. http://dx.doi.org/10.1142/9789812709745_0003.
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Hichem, Bennasr, and M’Sahli Faouzi. "An Optimization Procedure of Model’s Base Construction in Multimodel Representation of Complex Nonlinear Systems." In Optimization Problems in Engineering [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.96458.
Повний текст джерелаАнотація:
The multimodel approach is a research subject developed for modeling, analysis and control of complex systems. This approach supposes the definition of a set of simple models forming a model’s library. The number of models and the contribution of their validities is the main issues to consider in the multimodel approach. In this chapter, a new theoretical technique has been developed for this purpose based on a combination of probabilistic approaches with different objective function. First, the number of model is constructed using neural network and fuzzy logic. Indeed, the number of models is determined using frequency-sensitive competitive learning algorithm (FSCL) and the operating clusters are identified using Fuzzy K- means algorithm. Second, the Models’ base number is reduced. Focusing on the use of both two type of validity calculation for each model and a stochastic SVD technique is used to evaluate their contribution and permits the reduction of the Models’ base number. The combination of FSCL algorithms, K-means and the SVD technique for the proposed concept is considered as a deterministic approach discussed in this chapter has the potential to be applied to complex nonlinear systems with dynamic rapid. The recommended approach is implemented, reviewed and compared to academic benchmark and semi-batch reactor, the results in Models’ base reduction is very important witch gives a good performance in modeling.
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Trappenberg, Thomas P. "Cyclic models and recurrent neural networks." In Fundamentals of Machine Learning, 183–205. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198828044.003.0009.
Повний текст джерелаАнотація:
This chapter discusses models with cyclic dependencies. There are two principle architectures that are discussed. The first principle architecture of cyclic graphs comprises directed graphs similar to the Bayesian networks except that they include loops. Formally, such networks represent dynamical systems in the wider context and therefore represent some form of temporal modeling. The second type of models have connections between neurons that are bi-directional. These types of networks will be discussed in the context of stochastic units in the second half of this chapter.
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Toroczkai, Zoltan, and György Korniss. "Scalability, Random Surfaces, and Synchronized Computing Networks." In Computational Complexity and Statistical Physics. Oxford University Press, 2005. http://dx.doi.org/10.1093/oso/9780195177374.003.0020.
Повний текст джерелаАнотація:
In most cases, it is impossible to describe and understand complex system dynamics via analytical methods. The density of problems that are rigorously solvable with analytic tools is vanishingly small in the set of all problems, and often the only way one can reliably obtain a system-level understanding of such problems is through direct simulation. This chapter broadens the discussion on the relationship between complexity and statistical physics by exploring how the computational scalability of parallelized simulation can be analyzed using a physical model of surface growth. Specifically, the systems considered here are made up of a large number of interacting individual elements with a finite number of attributes, or local state variables, each assuming a countable number (typically finite) of values. The dynamics of the local state variables are discrete events occurring in continuous time. Between two consecutive updates, the local variables stay unchanged. Another important assumption we make is that the interactions in the underlying system to be simulated have finite range. Examples of such systems include: magnetic systems (spin states and spin flip dynamics); surface growth via molecular beam epitaxy (height of the surface, molecular deposition, and diffusion dynamics); epidemiology (health of an individual, the dynamics of infection and recovery); financial markets (wealth state, buy/sell dynamics); and wireless communications or queueing systems (number of jobs, job arrival dynamics). Often—as in the case we study here—the dynamics of such systems are inherently stochastic and asynchronous. The simulation of such systems is nontrivial, and in most cases the complexity of the problem requires simulations on distributed architectures, defining the field of parallel discrete-event simulations (PDES) [186, 367, 416]. Conceptually, the computational task is divided among n processing elements (PEs), where each processor evolves the dynamics of the allocated piece. Due to the interactions among the individual elements of the simulated system (spins, atoms, packets, calls, etc.) the PEs must coordinate with a subset of other PEs during the simulation. For example, the state of a spin can only be updated if the state of the neighbors is known. However, some neighbors might belong to the computational domain of another PE, thus, message passing will be required in order to preserve causality.
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Mainzer, Klaus. "Challenges of Complex Systems in Cognitive and Complex Systems." In Thinking Machines and the Philosophy of Computer Science, 367–84. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-61692-014-2.ch022.
Повний текст джерелаАнотація:
After an introduction (1) the article analyzes complex systems and the evolution of the embodied mind (2), complex systems and the innovation of embodied robotics (3), and finally discusses challenges of handling a world with increasing complexity: Large-scale networks have the same universal properties in evolution and technology (4). Considering the evolution of the embodied mind (2), we start with an introduction of complex systems and nonlinear dynamics (2.1), apply this approach to neural self-organization (2.2), distinguish degrees of complexity of the brain (2.3), explain the emergence of cognitive states by complex systems dynamics (2.4), and discuss criteria for modeling the brain as complex nonlinear system (2.5). The innovation of embodied robotics (3) is a challenge of complex systems and future technology. We start with the distinction of symbolic and embodied AI (3.1). Embodied robotics is inspired by the evolution of life. Modern systems biology integrates the molecular, organic, human, and ecological levels of life with computational models of complex systems (3.2). Embodied robots are explained as dynamical systems (3.3). Self-organization of complex systems needs self-control of technical systems (3.4). Cellular neural networks (CNN) are an example of self-organizing complex systems offering new avenues for neurobionics (3.5). In general, technical neural networks support different kinds of learning robots (3.6). Embodied robotics aims at the development of cognitive and conscious robots (3.7).
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Kuwahara, Hiroyuki, and Chris J. Myers. "Abstraction Methods for Analysis of Gene Regulatory Networks." In Handbook of Research on Computational Methodologies in Gene Regulatory Networks, 352–85. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-60566-685-3.ch015.
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With advances in high throughput methods of data collection for gene regulatory networks, we are now in a position to face the challenge of elucidating how these genes coupled with environmental stimuli orchestrate the regulation of cell-level behaviors. Understanding the behavior of such complex systems is likely impossible to achieve with wet-lab experiments alone due to the amount and complexity of the data being collected. Therefore, it is essential to integrate the experimental work with efficient and accurate computational methods for analysis. Unfortunately, such analysis is complicated not only by the sheer size of the models of interest but also by the fact that gene regulatory networks often involve small molecular counts making discrete and stochastic analysis necessary. To address this problem, this chapter presents a model abstraction methodology which systematically performs various model abstractions to reduce the complexity of computational biochemical models resulting in substantial improvements in analysis time with limited loss in accuracy.
Тези доповідей конференцій з теми "Complex systems, networks, dynamical models on networks, stochastic models"
1
Turner, Jonathan G., and Biswanath Samanta. "Nonlinear Control of Dynamic Systems Using Single Multiplicative Neuron Models." In ASME 2012 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/imece2012-87440.
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The paper presents an approach to nonlinear control of dynamic systems using artificial neural networks (ANN). A novel form of ANN, namely, single multiplicative neuron (SMN) model is proposed in place of more traditional multi-layer perceptron (MLP). SMN derives its inspiration from the single neuron computation model in neuroscience. SMN model is trained off-line, to estimate the network weights and biases, using a population based stochastic optimization technique, namely, particle swarm optimization (PSO). Both off-line training and on-line learning of SMN have been considered. The development of the control algorithm is illustrated through the hardware-in-the-loop (HIL) implementation of DC motor speed control in LabVIEW environment. The controller based on SMN performs better than MLP. The simple structure and faster computation of SMN have the potential to make it a preferred candidate for implementation of real-life complex control systems.
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Wang, Jia, Tong Sun, Benyuan Liu, Yu Cao, and Hongwei Zhu. "CLVSA: A Convolutional LSTM Based Variational Sequence-to-Sequence Model with Attention for Predicting Trends of Financial Markets." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/514.
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Financial markets are a complex dynamical system. The complexity comes from the interaction between a market and its participants, in other words, the integrated outcome of activities of the entire participants determines the markets trend, while the markets trend affects activities of participants. These interwoven interactions make financial markets keep evolving. Inspired by stochastic recurrent models that successfully capture variability observed in natural sequential data such as speech and video, we propose CLVSA, a hybrid model that consists of stochastic recurrent networks, the sequence-to-sequence architecture, the self- and inter-attention mechanism, and convolutional LSTM units to capture variationally underlying features in raw financial trading data. Our model outperforms basic models, such as convolutional neural network, vanilla LSTM network, and sequence-to-sequence model with attention, based on backtesting results of six futures from January 2010 to December 2017. Our experimental results show that, by introducing an approximate posterior, CLVSA takes advantage of an extra regularizer based on the Kullback-Leibler divergence to prevent itself from overfitting traps.
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Abdelbari, Hassan, and Kamran Shafi. "Optimising a constrained echo state network using evolutionary algorithms for learning mental models of complex dynamical systems." In 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 2016. http://dx.doi.org/10.1109/ijcnn.2016.7727822.
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Jeon, Soo. "State Estimation for Kinematic Model Over Lossy Network." In ASME 2010 Dynamic Systems and Control Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/dscc2010-4297.
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The major benefit of the kinematic Kalman filter (KKF), i.e., the state estimation based on kinematic model is that it is immune to parameter variations and unknown disturbances regardless of the operating conditions. In carrying out complex motion tasks such as the coordinated manipulation among multiple machines, some of the motion variables measured by sensors may only be available through the communication layer, which requires to formulate the optimal state estimator subject to lossy network. In contrast to standard dynamic systems, the kinematic model used in the KKF relies on sensory data not only for the output but also for the process input. This paper studies how the packet dropout occurring from the input sensor as well as the output sensor affects the performance of the KKF. When the output sensory data are delivered through the lossy network, it has been shown that the mean error covariance of the KKF is bounded for any non-zero packet arrival rate. On the other hand, if the input sensory data are subject to lossy network, the Bernoulli dropout model results in an unbounded mean error covariance. More practical strategy is to adopt the previous input estimate in case the current packet is dropped. For each case of packet dropout models, the stochastic characteristics of the mean error covariance are analyzed and compared. Simulation results are presented to illustrate the analytical results and to compare the performance of the time varying (optimal) filter gain with that of the static (sub-optimal) filter gain.
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Badihi, Hamed, Javad Soltani Rad, Youmin Zhang, and Henry Hong. "Data-Driven Model-Based Fault Diagnosis in a Wind Turbine With Actuator Faults." In ASME 2014 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/imece2014-38686.
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Wind turbines are renewable energy conversion devices that are being deployed in greater numbers. However, today’s wind turbines are still expensive to operate, and maintain. The reduction of operational and maintenance costs has become a key driver for applying low-cost, condition monitoring and diagnosis systems in wind turbines. Accurate and timely detection, isolation and diagnosis of faults in a wind turbine allow satisfactory accommodation of the faults and, in turn, enhancement of the reliability, availability and productivity of wind turbines. The so–called model-based Fault Detection and Diagnosis (FDD) approaches utilize system model to carry out FDD in real-time. However, wind turbine systems are driven by wind as a stochastic aerodynamic input, and essentially exhibit highly nonlinear dynamics. Accurate modeling of such systems to be suitable for use in FDD applications is a rather difficult task. Therefore, this paper presents a data-driven modeling approach based on artificial intelligence (AI) methods which have excellent capability in describing complex and uncertain systems. In particular, two data-driven dynamic models of wind turbine are developed based on Fuzzy Modeling and Identification (FMI) and Artificial Neural Network (ANN) methods. The developed models represent the normal operating performance of the wind turbine over a full range of operating conditions. Consequently, a model-based FDD scheme is developed and implemented based on each of the individual models. Finally, the FDD performance is evaluated and compared through a series of simulations on a well-known large offshore wind turbine benchmark in the presence of wind turbulences, measurement noises, and different realistic fault scenarios in the generator/converter torque actuator.
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Safarkhani, Salar, Ilias Bilionis, and Jitesh H. Panchal. "Understanding the Effect of Task Complexity and Problem-Solving Skills on the Design Performance of Agents in Systems Engineering." In ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/detc2018-85941.
Повний текст джерелаАнотація:
Systems engineering processes coordinate the efforts of many individuals to design a complex system. However, the goals of the involved individuals do not necessarily align with the system-level goals. Everyone, including managers, systems engineers, subsystem engineers, component designers, and contractors, is self-interested. It is not currently understood how this discrepancy between organizational and personal goals affects the outcome of complex systems engineering processes. To answer this question, we need a systems engineering theory that accounts for human behavior. Such a theory can be ideally expressed as a dynamic hierarchical network game of incomplete information. The nodes of this network represent individual agents and the edges the transfer of information and incentives. All agents decide independently on how much effort they should devote to a delegated task by maximizing their expected utility; the expectation is over their beliefs about the actions of all other individuals and the moves of nature. An essential component of such a model is the quality function, defined as the map between an agent’s effort and the quality of their job outcome. In the economics literature, the quality function is assumed to be a linear function of effort with additive Gaussian noise. This simplistic assumption ignores two critical factors relevant to systems engineering: (1) the complexity of the design task, and (2) the problem-solving skills of the agent. Systems engineers establish their beliefs about these two factors through years of job experience. In this paper, we encode these beliefs in clear mathematical statements about the form of the quality function. Our approach proceeds in two steps: (1) we construct a generative stochastic model of the delegated task, and (2) we develop a reduced order representation suitable for use in a more extensive game-theoretic model of a systems engineering process. Focusing on the early design stages of a systems engineering process, we model the design task as a function maximization problem and, thus, we associate the systems engineer’s beliefs about the complexity of the task with their beliefs about the complexity of the function being maximized. Furthermore, we associate an agent’s problem solving-skills with the strategy they use to solve the underlying function maximization problem. We identify two agent types: “naïve” (follows a random search strategy) and “skillful” (follows a Bayesian global optimization strategy). Through an extensive simulation study, we show that the assumption of the linear quality function is only valid for small effort levels. In general, the quality function is an increasing, concave function with derivative and curvature that depend on the problem complexity and agent’s skills.
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Panizza, Andrea, Alessio Bonini, and Luca Innocenti. "Uncertainty Quantification of Hot Gas Ingestion for a Gas Turbine Nozzle Using Polynomial Chaos." In ASME Turbo Expo 2015: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/gt2015-42679.
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One of the most critical parameters in the design process of cooled hot gas components, is the Back Flow Margin (BFM). This dimensionless parameter quantifies the margin to hot gas ingestion through a cooled component wall. A correct evaluation of this parameter is crucial in order to avoid component failure. In presence of combustion chambers that exhibit low pressure losses, BFM becomes one of the most restrictive requirements in the thermal design of cooled components. In this work, a conceptual BFM assessment of the first nozzle of an HP gas turbine is described. The component is subject to the highest thermal load; complex cooling systems are required to ensure an acceptable metal temperature and to match life time requirement. Due to manufacturing tolerances and fluid dynamic uncertainties, hot gas ingestion events are possible also for a nozzle that exhibits BFM higher than zero in nominal conditions, even if with a low probability. Here, the cooling scheme of the nozzle is modeled using an in-house fluid network tool that allows a quick and accurate computation of the equivalent cooling scheme and thus the occurrence of hot gas ingestion, corresponding to a negative flow rate in one of the cooling sub-models. However, as the probability of hot gas ingestion is rather small, an accurate estimation of this event based on the standard Monte Carlo method requires a huge number of runs. A more efficient estimation of this probability can be obtained using stochastic expansion methods, such as the Polynomial Chaos Expansion. Pseudospectral approximations based on either a tensor-product expansion or the Sparse Pseudospectral Approximation Method (SPAM) are used, in order to estimate the probability of hot gas ingestion and the sensitivity to random parameters. The results are compared with those coming from Monte Carlo method, showing the superior accuracy of the stochastic expansion methods.
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Jha, Sumit, Rickard Ewetz, Alvaro Velasquez, and Susmit Jha. "On Smoother Attributions using Neural Stochastic Differential Equations." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/73.
Повний текст джерелаАнотація:
Several methods have recently been developed for computing attributions of a neural network's prediction over the input features. However, these existing approaches for computing attributions are noisy and not robust to small perturbations of the input. This paper uses the recently identified connection between dynamical systems and residual neural networks to show that the attributions computed over neural stochastic differential equations (SDEs) are less noisy, visually sharper, and quantitatively more robust. Using dynamical systems theory, we theoretically analyze the robustness of these attributions. We also experimentally demonstrate the efficacy of our approach in providing smoother, visually sharper and quantitatively robust attributions by computing attributions for ImageNet images using ResNet-50, WideResNet-101 models and ResNeXt-101 models.
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Mehrpouyan, Hoda, Brandon Haley, Andy Dong, Irem Y. Tumer, and Chris Hoyle. "Resilient Design of Complex Engineered Systems Against Cascading Failure." In ASME 2013 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/imece2013-63308.
Повний текст джерелаАнотація:
This paper describes an approach commonly used with complex networks to study the failure propagation in an engineered system design. The goal of the research is to synthesize and illustrate system design characteristics that results from possible impact of the underlying design methodology based on cascading failures. Further, identifying the most vulnerable component in the design or system design architectures that are resilient to such dissemination of failures provide additional property improvement for resilient design. The paper presents a case study based on the ADAPT (Electrical Power System) EPS testbed at NASA Ames as a subsystem for the Ramp System of an Infantry Fighting Vehicle (IFV). A popular methodology based on the adjacency matrix, which is commonly used to represent edge connections between nodes in complex networks, has inspired interest in the use of similar methods to represent complex engineered systems. This is made possible, by defining the connections between components as a flow of energy, signal, and material and constraining physical connection between compatible components within complex engineered systems. Non-linear dynamical system (NLDS) and epidemic spreading models are used to compare the failure propagation mean time transformation. The results show that coupling, modularity, and module complexity all play an important part in the design of robust large complex engineered systems.
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Hall, Daniel L., and Biswanath Samanta. "Nonlinear Control of a Magnetic Levitation System Using Single Multiplicative Neuron Models." In ASME 2013 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/imece2013-64066.
Повний текст джерелаАнотація:
The paper presents an approach to nonlinear control of a magnetic levitation system using artificial neural networks (ANN). A novel form of ANN, namely, single multiplicative neuron (SMN) model is proposed in place of more traditional multi-layer perceptron (MLP). SMN derives its inspiration from the single neuron computation model in neuroscience. SMN model is trained off-line, to estimate the network weights and biases, using a population based stochastic optimization technique, namely, particle swarm optimization (PSO). Both off-line training and on-line learning of SMN have been considered. The ANN based techniques have been compared with a feedback linearization approach. The development of the control algorithms is illustrated through the hardware-in-the-loop (HIL) implementation of magnetic levitation in LabVIEW environment. The controllers based on ANN performed quite well and better than the one based on feedback linearization. However, the SMN structure was much simpler than the MLP for similar performance. The simple structure and faster computation of SMN have the potential to make it a preferred candidate for implementation of real-life complex magnetic levitation systems.