scholarly journals Non-homogeneous Heating induces Scale-free Correlations and Slow time Scales in a Granular-like Velocity Field

2021 ◽  
Author(s):  
Andrea Plati ◽  
Andrea Puglisi
Keyword(s):  
Velocity Field ◽  
Statistical Physics ◽  
Spatial Correlations ◽  
Finite Characteristic ◽  
Long Range Correlations ◽  
Scale Free ◽  
Temporal Correlations ◽  
Correlation Decay ◽  
Free Cooling

Abstract We consider a velocity field with linear viscous interactions defined on a one dimensional lattice. Brownian baths with different parameters can be coupled to the boundary sites and to the bulk sites, determining different kinds of non-equilibrium steady states or free-cooling dynamics. Analytical results for spatial and temporal correlations are provided by analytical diagonalisation of the system’s equations in the infinite size limit. We demonstrate that spatial correlations are scale-free and timescales become exceedingly long when the system is driven only at the boundaries. On the contrary, in the case a bath is coupled to the bulk sites too, an exponential correlation decay is found with a finite characteristic length. This is also true in the free cooling regime, but in this case the correlation length grows diffusively in time. We discuss the crucial role of non-homogeneous energy injection for long-range correlations and slow timescales , proposing an analogy between this simplified dynamical model and recent experiments with dense vibro-fluidized granular materials. Several generalizations and connections with the statistical physics of active matter are also suggested.

scholarly journals Long range correlations and slow time scales in a boundary driven granular model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Andrea Plati ◽  
Andrea Puglisi
Keyword(s):  
Long Range ◽  
Time Scales ◽  
Statistical Physics ◽  
Spatial Correlations ◽  
Slow Time ◽  
Finite Characteristic ◽  
Long Range Correlations ◽  
Scale Free ◽  
Temporal Correlations ◽  
Free Cooling

AbstractWe consider a velocity field with linear viscous interactions defined on a one dimensional lattice. Brownian baths with different parameters can be coupled to the boundary sites and to the bulk sites, determining different kinds of non-equilibrium steady states or free-cooling dynamics. Analytical results for spatial and temporal correlations are provided by analytical diagonalisation of the system’s equations in the infinite size limit. We demonstrate that spatial correlations are scale-free and time-scales become exceedingly long when the system is driven only at the boundaries. On the contrary, in the case a bath is coupled to the bulk sites too, an exponential correlation decay is found with a finite characteristic length. This is also true in the free cooling regime, but in this case the correlation length grows diffusively in time. We discuss the crucial role of boundary driving for long-range correlations and slow time-scales, proposing an analogy between this simplified dynamical model and dense vibro-fluidized granular materials. Several generalizations and connections with the statistical physics of active matter are also suggested.

Reducing multipath effect of low-cost GNSS receivers for monitoring by considering temporal correlations

2020 ◽  
Vol 14 (2) ◽  
pp. 167-175
Author(s):  
Li Zhang ◽  
Volker Schwieger
Keyword(s):  
Low Cost ◽  
Ground Plane ◽  
Single Frequency ◽  
Spatial Correlations ◽  
Raw Data ◽  
Multipath Effect ◽  
Temporal Correlations ◽  
Multipath Effects ◽  
Gnss Receivers ◽  
Multipath Signals

AbstractThe investigations on low-cost single frequency GNSS receivers at the Institute of Engineering Geodesy (IIGS) show that u-blox GNSS receivers combined with low-cost antennas and self-constructed L1-optimized choke rings can reach an accuracy which almost meets the requirements of geodetic applications (see Zhang and Schwieger [25]). However, the quality (accuracy and reliability) of low-cost GNSS receiver data should still be improved, particularly in environments with obstructions. The multipath effects are a major error source for the short baselines. The ground plate or the choke ring ground plane can reduce the multipath signals from the horizontal reflector (e. g. ground). However, the shieldings cannot reduce the multipath signals from the vertical reflectors (e. g. walls).Because multipath effects are spatially and temporally correlated, an algorithm is developed for reducing the multipath effect by considering the spatial correlations of the adjoined stations (see Zhang and Schwieger [24]). In this paper, an algorithm based on the temporal correlations will be introduced. The developed algorithm is based on the periodic behavior of the estimated coordinates and not on carrier phase raw data, which is easy to use. Because, for the users, coordinates are more accessible than the raw data. The multipath effect can cause periodic oscillations but the periods change over time. Besides this, the multipath effect’s influence on the coordinates is a mixture of different multipath signals from different satellites and different reflectors. These two properties will be used to reduce the multipath effect. The algorithm runs in two steps and iteratively. Test measurements were carried out in a multipath intensive environment; the accuracies of the measurements are improved by about 50 % and the results can be delivered in near-real-time (in ca. 30 minutes), therefore the algorithm is suitable for structural health monitoring applications.

scholarly journals Role of packing density and spatial correlations in strongly scattering 3D systems

Optica ◽  
2018 ◽  
Vol 5 (9) ◽  
pp. 1037 ◽  
Author(s):  
Lorenzo Pattelli ◽  
Amos Egel ◽  
Uli Lemmer ◽  
Diederik S. Wiersma
Keyword(s):  
Packing Density ◽  
Spatial Correlations

Locally Recurrent Neural Networks and Their Applications

2010 ◽  
pp. 195-222
Author(s):  
Todor D. Ganchev
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Computational Models ◽  
Machine Learning Techniques ◽  
Spatial Correlations ◽  
Temporal Correlations ◽  
Learning Techniques ◽  
Locally Recurrent ◽  
The One ◽  
And Control

In this chapter we review various computational models of locally recurrent neurons and deliberate the architecture of some archetypal locally recurrent neural networks (LRNNs) that are based on them. Generalizations of these structures are discussed as well. Furthermore, we point at a number of realworld applications of LRNNs that have been reported in past and recent publications. These applications involve classification or prediction of temporal sequences, discovering and modeling of spatial and temporal correlations, process identification and control, etc. Validation experiments reported in these developments provide evidence that locally recurrent architectures are capable of identifying and exploiting temporal and spatial correlations (i.e., the context in which events occur), which is the main reason for their advantageous performance when compared with the one of their non-recurrent counterparts or other reasonable machine learning techniques.

Role of Cutoff Scaling in Scale-Free Networks

2018 ◽  
Vol 68 (5) ◽  
pp. 563-569
Author(s):  
Meesoon HA*
Keyword(s):  
Scale Free ◽  
Scale Free Networks

scholarly journals Numerical Precision Effects on GPU Simulation of Massive Spatial Data, Based on the Modified Planar Rotator Model

2020 ◽  
Vol 226 ◽  
pp. 02015
Author(s):  
Matúš Lach ◽  
Michal Borovský ◽  
Milan Žukovič
Keyword(s):  
Spatial Data ◽  
Statistical Physics ◽  
Parallel Implementation ◽  
Prediction Method ◽  
Spatial Prediction ◽  
Spin Model ◽  
Prediction Algorithm ◽  
Data Sets ◽  
Spatial Correlations ◽  
Spin Pair

The present research builds on a recently proposed spatial prediction method for discretized two-dimensional data, based on a suitably modified planar rotator (MPR) spin model from statistical physics. This approach maps the measured data onto interacting spins and, exploiting spatial correlations between them, which are similar to those present in geostatistical data, predicts the data at unmeasured locations. Due to the shortrange nature of the spin pair interactions in the MPR model, parallel implementation of the prediction algorithm on graphical processing units (GPUs) is a natural way of increasing its efficiency. In this work we study the effects of reduced computing precision as well as GPU-based hardware intrinsic functions on the speedup and accuracy of the MPR-based prediction and explore which aspects of the simulation can potentially benefit the most from the reduced precision. It is found that, particularly for massive data sets, a thoughtful precision setting of the GPU implementation can significantly increase the computational efficiency, while incurring little to no degradation in the prediction accuracy.

scholarly journals Connectivity, Information Jumps, and Market Stability: An Agent-Based Approach

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Khaldoun Khashanah ◽  
Talal Alsulaiman
Keyword(s):  
Free Market ◽  
Agent Based ◽  
Scale Free ◽  
Jump Size ◽  
Inaccurate Information ◽  
Volatility Index ◽  
Market Stability ◽  
Information Awareness ◽  
Simulated Market

We propose a metamodel to assess simulated market stability by introducing information connectivity in an agent-based network. The market is occupied by heterogeneous agents with different behaviors, strategies, and information connectivity. A jump-diffusion process simulating events that may occur in the market is introduced. Agents information awareness varies along with agents propensity to respond to the information jump and jump size. A jump reshuffles market positions based on agents risk preferences determined by behavior and strategy. We examine the effect of information awareness on the volatility index of the simulated market in a scale-free market network. The analysis is performed by developing five experiments wherein the first one corresponds to systemic information ignorance state. Three experiments examine the role of hubs, normal agents, and hermits in the network when intermediate combinations of agent types have information awareness. The fifth experiment corresponds to the systemic information awareness with all agents being informed. The results show that the simulated market is driven to instability in a similar manner to patterns observed in a crisis where all agents become homogeneous in information awareness of events. Hubs contribute to increased connectivity and act as amplifiers of good, bad, or inaccurate information or sentiment.

scholarly journals Two adaptive radiative transfer schemes for numerical weather prediction models

2007 ◽  
Vol 7 (21) ◽  
pp. 5659-5674 ◽  
Author(s):  
V. Venema ◽  
A. Schomburg ◽  
F. Ament ◽  
C. Simmer
Keyword(s):  
Radiative Transfer ◽  
Prediction Models ◽  
Weather Prediction ◽  
Limited Area ◽  
Perturbation Scheme ◽  
Spatial Correlations ◽  
Radiative Fluxes ◽  
Temporal Correlations ◽  
Core Idea ◽  
Computational Resources

Abstract. Radiative transfer calculations in atmospheric models are computationally expensive, even if based on simplifications such as the δ-two-stream approximation. In most weather prediction models these parameterisation schemes are therefore called infrequently, accepting additional model error due to the persistence assumption between calls. This paper presents two so-called adaptive parameterisation schemes for radiative transfer in a limited area model: A perturbation scheme that exploits temporal correlations and a local-search scheme that mainly takes advantage of spatial correlations. Utilising these correlations and with similar computational resources, the schemes are able to predict the surface net radiative fluxes more accurately than a scheme based on the persistence assumption. An important property of these adaptive schemes is that their accuracy does not decrease much in case of strong reductions in the number of calls to the δ-two-stream scheme. It is hypothesised that the core idea can also be employed in parameterisation schemes for other processes and in other dynamical models.

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