This paper studies the density regulation problem for a large-scale robotic swarm with homogeneous agents. A novel robust model predictive mean-field control method is proposed to significantly ...improve the control performance and the algorithm scalability with the population size. To that end, a top-down control philosophy is first employed, under which the physical space is divided into finite disjoint bins and the actual density distribution (ADD) over these bins is taken as the macrostate. The system dynamics of ADD is then established by including a stochastic disturbance in the mean-field model, which describes the evolution process of each agent’s probability density distribution over the bins. Next, an optimization-based model predictive control method is developed to efficiently overcome the performance degradation raised by the disturbance term. Furthermore, the conditions ensuring the algorithm feasibility are strictly developed. We also prove that the actual swarm density converges to the target one in probability. Finally, simulation and comparison studies illustrate the effectiveness and advantages of the proposed algorithm over the existing results.
The ability to predict surface reactions on heterogeneous nanoparticle catalysts is important for the design and development of high-performance materials and for controlling the reaction conditions. ...Herein, we formulate a mean-field model of interacting reaction intermediates involving subsurface species. The model uses a partition function based on a statistical lattice gas model to describe the surface phase, with thermodynamic relationships satisfied using the derived intermediates. Kinetic rate equations were systematically formulated, aided by linear relationships between activation barriers and reaction free energies. The model was applied to oxide growth on a Pt electrocatalyst for use in proton-exchange membrane fuel cells, which demonstrated that the model reasonably reproduces the experimentally measured amounts of oxide formed over a wide time range. Kinetic simulations and density functional theory calculations consistently indicate the significance of the self-stabilizing interactions of subsurface oxides in accurately simulating Pt-oxide formation and reduction.
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Classical approaches to experimental design assume that intervening on one unit does not affect other units. There are many important settings, however, where this noninterference assumption does not ...hold, as when running experiments on supply-side incentives on a ride-sharing platform or subsidies in an energy marketplace. In this paper, we introduce a new approach to experimental design in large-scale stochastic systems with considerable cross-unit interference, under an assumption that the interference is structured enough that it can be captured via mean-field modeling. Our approach enables us to accurately estimate the effect of small changes to system parameters by combining unobtrusive randomization with lightweight modeling, all while remaining in equilibrium. We can then use these estimates to optimize the system by gradient descent. Concretely, we focus on the problem of a platform that seeks to optimize supply-side payments
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in a centralized marketplace where different suppliers interact via their effects on the overall supply-demand equilibrium, and we show that our approach enables the platform to optimize
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in large systems using vanishingly small perturbations.
This paper was accepted by Hamid Nazerzadeh, big data analytics.
Explaining the mechanisms of dynamic memory, that allows for a temporary storage of information at the timescale of seconds despite the neuronal firing at the millisecond scale, is an important ...challenge not only for neuroscience, but also for computation in neuromorphic artificial networks. We demonstrate the potential origin of such longer timescales by comparing the spontaneous activity in excitatory neural networks with sparse random, regular and small-world connection topologies. We derive a mean-field model based on a self-consistent approach and white noise approximation to analyze the transient and long-term collective network dynamics. While the long-term dynamics is typically irregular and weakly correlated independent of the network architecture, especially long timescales are revealed for the transient activity comprised of switching fronts in regular and small-world networks with a small rewiring probability. Analyzing the dynamic memory of networks in performing a simple computational delay task within the framework of reservoir computing, we show that an optimal performance on average is reached for a regular connection topology if the input is appropriately structured, but certain instances of small-world networks may strongly deviate from configuration averages and outperform all the other considered network architectures.
•Dynamic memory stores information for seconds despite much faster neuronal spiking.•Emergence of long timescales examined for random, regular and small-world networks.•Derived mean-field model to characterize transient and long-term network dynamics.•Dynamic memory examined for a delay task within reservoir computing framework.•Dynamic memory found to be optimal for regular networks with structured input.
In this study, we report the effect of Bi doping on the critical behavior and the magnetocaloric properties of Pr0.8−xBixSr0.2MnO3 (x = 0, 0.05 and 0.1) structures prepared by sol-gel method. Refined ...values of the critical exponents β, γ, and δ determined from the modified Arrott plots and Kouvel–Fisher method show that the parent sample is described by the tricritical mean field model whereas the 3D-Ising model is the best model describing doped samples. Consequently, a transition from long-range to short range FM interactions caused by the introduction of Bi in the parent sample can be identified. All samples reveal the presence of some ferromagnetic domains in the paramagnetic phase. In the vicinity of the Curie temperature TC and for an applied magnetic field of 5 T, the maximum of magnetic entropy change |ΔSM| decreases from 5.41 J K−1kg−1 for x = 0–3.11 J K−1kg−1 for x = 0.1, respectively. The |ΔSM| for doped samples exhibits a large broad variation with temperature around TC as compared with the parent sample. Such effect is substantially advantageous for magnetic refrigeration. Also, the magnetocaloric effect is investigated using Landau theory. A good agreement is obtained between this theory and experimental data, which suggests that electronic interactions and magnetoelastic coupling are at the origin of the magnetocaloric properties of our samples. In addition, we report that |ΔSM| curves follow the universal law confirming the second-ordered paramagnetic-ferromagnetic transition.
•Critical behavior of Pr0.8−xBixSr0.2MnO3 (x = 0, 0.05 and 0.1) is studied.•The magnetocaloric properties of these compounds are also investigated.•A large magnetic entropy change |ΔSM| is obtained for all prepared samples.•|ΔSM| curves confirm the second-order PM-FM transition.
Agglomeration and interphase region of fillers are two important factors that affect the mechanical properties of metal matrix composites reinforced with carbon nanotubes (CNT-CMMs). However, most of ...the existing theoretical models predict an ascending linear in strength for composites with increasing filler content, which disagrees with the experimental results, especially at high filler loading. In fact, at high CNT concentrations, agglomeration and weak interphase region bonding reduce the strength and consequently degrade the mechanical properties of composites. Based on the mean-field theory, we present a novel micromechanical model to predict the elastic modulus of CNT-CMMs by considering the effects of these two factors. Furthermore, we investigate the effect of other parameters such as CNTs aspect ratio, agglomeration amount, interphase layer thickness and modulus, and matrix modulus on the elastic modulus of CNT-CMMs. Finally, we validate our model by comparing it with numerous experimental outcomes from the literature signifies good precision. Using this model, it is possible to optimize the filler value and also maximize the elastic modulus, which can be a powerful tool for designing the CNT-CMMs.
The effect of temperature on the volume and surface contributions in the nuclear symmetry energy and their ratio in the isotopic chains of rare earth Nd, Sm, Gd, and Dy nuclei with N = 82-126 is ...analyzed in the framework of coherent density fluctuation model (CDFM). The weight function of nuclei, within CDFM, are calculated by using the densities from the temperature-dependent relativistic mean-field (RMF) model. Firstly, we discuss the temperature-dependence of bulk properties of nuclei, within RMF model, such as binding energy, deformation parameter, charge radius and isotopic shift along with comparison with the available experimental data at temperature T=0 MeV. Further, we discuss the thermal evolution of symmetry energy and its volume and surface components. At T=0 MeV, the persistence of a peak in the symmetry energy and components at neutron number N=100 shows the manifestation of deformed shell closure in consonance with an earlier study by one of us L. Satpathy, S.K. Patra, J. Phys. G 30 (2004) 771; S.K. Ghorui, et al., Phys. Rev. C 85 (2012) 064327. However, the scenario changes with rise in temperature and the magnitude of peak decreases at higher temperatures. At T=3 MeV, the peak disappears which may be due to shape change in addition to quenching of shell effects since the quadrupole deformation parameter β2 decreases with an increase in temperature and nuclei become spherical at T=3 MeV. It indicates that behavior of symmetry energy is closely related to the deformation/shape of the nuclei. We have also discussed the values of volume symmetry energy, surface symmetry energy and their ratio which are in consonance with available experimental data.
Tungsten is the privileged option as plasma-facing material for the divertor region of ITER and DEMO tokamaks. Under repeated high thermal fluxes (10 MW/m² in steady state and 20 MW/m² in ...quasi-steady state), Plasma-Facing Units (PFU) can be damaged through thermal fatigue phenomena, which are enhanced by in-service tungsten recrystallization. Slowing down tungsten recrystallization is thus a way to optimize PFU lifetime. Tungsten-recrystallization kinetics is known to depend upon the initial microstructure. In the present paper, a mean-field model 1 is developed to highlight microstructural effects upon tungsten restoration mechanisms at high temperature. The effects of grain size, initial recrystallized fraction, initial dislocation density, i.e. stored energy, and dislocation density distribution have been investigated. It is clearly quantified how a higher stored energy can lead to a drastic increase of the recrystallization rate. It is also shown how a prior recovery stage helps to slow down recrystallization. Finally, a conclusion is brought upon the ability of such a mean field model to optimize the tungsten initial microstructure for fusion engineering.
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