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DTSTART;TZID=Europe/Paris:20180607T130000
DTEND;TZID=Europe/Paris:20180607T140000
DTSTAMP:20260404T100651
CREATED:20180418T213241Z
LAST-MODIFIED:20180501T114421Z
UID:398-1528376400-1528380000@www.naxys.be
SUMMARY:Ludovic Renson (University of Bristol\, UK)
DESCRIPTION:Title: Exploring the dynamics of nonlinear experiments using control-based continuation \nAbstract: With the constant drive for better performance and efficiency\, technological boundaries are being pushed to their limits. In mechanics\, this often means that the dynamic behaviour of structures becomes increasingly nonlinear. Nonlinearity can arise\, for instance\, from the large displacements and rotations of flexible components (such as blades in wind turbines). \nWhile a significant effort has been\, and is\, devoted to the mathematical modelling and numerical analysis of such systems\, relatively little research addresses the issue of rigourous experimental testing. In fact\, until now\, there has been no general\, systematic method that can directly measure and characterise nonlinear dynamic behaviour during laboratory tests. Nonlinear systems are still tested as linear ones. Time series are collected for a whole range of excitation parameters and one relies on post-processing tools to understand the behaviour of the system. So far\, this sort of approach has not allowed quantitative comparisons between experiments and mathematical models; hence it is extremely challenging to incorporate nonlinear features into model development and validation processes. \nIn this talk\, I will present a method\, control-based continuation (CBC)\, which uses sensors and actuators to intelligently probe a physical system. Combining feedback control with numerical continuation algorithms\, CBC modifies\, on-line\, the excitation applied to the system in order to isolate the nonlinear behaviour of interest. In this way\, CBC offers the best conditions to analyse these dynamic features in detail\, to follow them as inputs and controllable parameters are changed\, and to detect and track boundaries between qualitatively different types of behaviour.
URL:https://www.naxys.be/event/ludovic-renson-university-of-bristol-uk/
LOCATION:E25
CATEGORIES:NAXYS Seminar
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DTSTART;TZID=Europe/Paris:20180619T130000
DTEND;TZID=Europe/Paris:20180619T140000
DTSTAMP:20260404T100651
CREATED:20180521T090151Z
LAST-MODIFIED:20180521T090151Z
UID:409-1529413200-1529416800@www.naxys.be
SUMMARY:Takaaki Aoki (Kagawa University\, Japan)
DESCRIPTION:Title: Cities and roads as pattern formation of their co-evolving dynamics on real-world landscape\n\n\nAbstract: Cities and their inter-connected transport networks form part of the fundamental infrastructure developed by human societies. Their organisation reflects a complex interplay between many natural and social factors\, including inter alia natural resources\, landscape\, and climate on the one hand\, combined with business\, commerce\, politics\, diplomacy and culture on the other. Nevertheless\, despite this complexity\, there has been some success in capturing key aspects of city growth and network formation in relatively simple models that include non-linear positive feedback loops. However\, these models are typically embedded in an idealised\, homogeneous space\, leading to regularly-spaced\, lattice-like distributions arising from Turing-type pattern formation. Here we argue that the geographical landscape plays a much more dominant\, but neglected role in pattern formation. To examine this hypothesis\, we evaluate the weighted distance between locations based on a least cost path across the natural terrain\, determined from high-resolution digital topographic databases for the Hokkaido region of Japan. These weights are included in a co-evolving\, dynamical model of both population aggregation in cities\, and movement via an evolving transport network. We compare the results from the stationary state of the system with current population distributions from census data\, and show a reasonable fit\, both qualitatively and quantitatively\, compared with models in homogeneous space. Thus we infer that that addition of weighted topography from the natural landscape to these models is both necessary and almost sufficient to reproduce the majority of the real-world spatial pattern of city sizes and locations in this example.
URL:https://www.naxys.be/event/takaaki-aoki-kagawa-university-japan/
LOCATION:E25
CATEGORIES:NAXYS Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20180621T130000
DTEND;TZID=Europe/Paris:20180621T140000
DTSTAMP:20260404T100651
CREATED:20180521T090404Z
LAST-MODIFIED:20180528T115337Z
UID:414-1529586000-1529589600@www.naxys.be
SUMMARY:Eleonora Andreotti (Università di Torino\, Italy)
DESCRIPTION:Title: Statistical properties of non linear random walks on networks \nAbstract: Random walks simulate various interacting entities (the nodes) which exchange ’particles’ according to the topological structure defined by the links and they are considered to introduce a dynamics on networks. These stochastic dynamical systems can be applied to model complex systems like transportation networks\, ecological systems\, neural networks\, economic systems etc.. In the linear case the statistical properties are defined by single particles dynamics and the equilibrium distribution depends on the network structure. We introduce non linear effects by assuming a finite transportation capacity of the links or a finite capacity in the nodes. As a consequence the transition probabilities depend on the dynamical state of the network and one cannot derive statistical properties of the system from single particle dynamics. We show that non-linear effects can be described by introducing an entropic force among the node states which allows to derive a master equation for the evolution of the probability distribution of the node population. This entropic force has a relevant effect on stationary distribution and the relaxation time scale depends on the numerosity of the population so that the thermodynamics limit is non trivial and the lifetime of the transient state is very long. Using numerical simulation we derive an analytical form of the entropic force and we study the dependence of the stationary distribution on the network topology.
URL:https://www.naxys.be/event/eleonora/
LOCATION:E25
CATEGORIES:NAXYS Seminar
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