具有Dzyaloshinskii-Moriya作用的Blume-Capel模型的临界性质

应用二自旋集团平均场近似的方法,研究了蜂窝晶格和正方晶格上具有Dzyaloshinskii-Moriya(DM)作用的Blume-Capel模型的临界性质,得到了该系统的相图。结果表明,所研究系统存在三临界点,并且三临界温度不随DM作用参量单调变化,三临界温度有最小值。系统的这种临界性质是交换耦合作用、晶体场作用和DM作用三者相互竞争的结果。     

Forward and Backward Mean-Field Stochastic Partial Differential Equation and Optimal Control

This paper is mainly concerned with the solutions to both forward and backward mean-field stochastic partial differential equation and the corresponding optimal control problem for mean-field stochastic partial differential equation. The authors first prove the continuous dependence theorems of forward and backward mean-field stochastic partial differential equations and show the existence and uniqueness of solutions to them. Then they establish necessary and sufficient optimality conditions of the control problem in the form of Pontryagin''s maximum principles. To illustrate the theoretical results, the authors apply stochastic maximum principles to study the infinite-dimensional linear-quadratic control problem of mean-field type. Further, an application to a Cauchy problem for a controlled stochastic linear PDE of mean-field type is studied.     

A second-moment incremental formulation for the mean-field homogenization of elasto-plastic composites

In this paper, the incremental formulation for the mean-field homogenization (MFH) of elasto-plastic composites is enriched by including second statistical moments of per-phase strain increment fields, thus combining two advantages. The first one is to handle non-monotonic loading histories and the second is to better account for the heterogeneity of microscopic fields. The proposal is currently restricted to elasto-plasticity with J2 flow theory in each phase, under the small perturbation hypothesis. The formulation crucially exploits the return mapping algorithm for the J2 model, with its two steps: elastic predictor, and plastic corrections. It is shown that the second-moment measure of the average von Mises stress in each phase at the elastic predictor step plays a major role in the computation of both the average stress and the comparison tangent operator. The proposal is implemented for an extended Mori-Tanaka scheme. Predictions are compared to results provided by full-field, finite element computations of representative volume elements or unit cells, for various composite materials, with polymer or metal matrices. There are cases where the predictions of the proposed modeling improve significantly over those of a first-order incremental formulation.     

A framework and a mean-field algorithm for the local control of spatial processes

The Markov Decision Process (MDP) framework is a tool for the efficient modelling and solving of sequential decision-making problems under uncertainty. However, it reaches its limits when state and action spaces are large, as can happen for spatially explicit decision problems. Factored MDPs and dedicated solution algorithms have been introduced to deal with large factored state spaces. But the case of large action spaces remains an issue. In this article, we define graph-based Markov Decision Processes (GMDPs), a particular Factored MDP framework which exploits the factorization of the state space and the action space of a decision problem. Both spaces are assumed to have the same dimension. Transition probabilities and rewards are factored according to a single graph structure, where nodes represent pairs of state/decision variables of the problem. The complexity of this representation grows only linearly with the size of the graph, whereas the complexity of exact resolution grows exponentially. We propose an approximate solution algorithm exploiting the structure of a GMDP and whose complexity only grows quadratically with the size of the graph and exponentially with the maximum number of neighbours of any node. This algorithm, referred to as MF-API, belongs to the family of Approximate Policy Iteration (API) algorithms. It relies on a mean-field approximation of the value function of a policy and on a search limited to the suboptimal set of local policies. We compare it, in terms of performance, with two state-of-the-art algorithms for Factored MDPs: SPUDD and Approximate Linear Programming (ALP). Our experiments show that SPUDD is not generally applicable to solving GMDPs, due to the size of the action space we want to tackle. On the other hand, ALP can be adapted to solve GMDPs. We show that ALP is faster than MF-API and provides solutions of similar quality for most problems. However, for some problems MF-API provides significantly better policies, and in all cases provides a better approximation of the value function of approximate policies. These promising results show that the GMDP model offers a convenient framework for modelling and solving a large range of spatial and structured planning problems, that can arise in many different domains where processes are managed over networks: natural resources, agriculture, computer networks, etc.     

An alternative formulation of rigorous mean-field theory

It is shown how to derive rigorous mean-field theory from a type of many-body interaction.     

混合量子-经典非绝热动力学理论与应用

Electronically non-adiabatic processes are essential parts of photochemical process, collisions of excited species, electron transfer processes, and quantum information processing. Various non-adiabatic dynamics methods and their numerical implementation have been developed in the last decades. This review summarizes the most significant development of mixed quantum-classical methods and their applications which mainly include the Liouville equation, Ehrenfest mean-field, trajectory surface hopping, and multiple spawning methods. The recently developed quantum trajectory mean-field method that accounts for the decoherence corrections in a parameter-free fashion is discussed in more detail.     

Modelling the effect of heterogeneous vaccination on metapopulation epidemic dynamics

Vaccination as an epidemic control strategy has a significant effect on epidemic spreading. In this paper, we propose a novel epidemic spreading model on metapopulation networks to study the impact of heterogeneous vaccination on epidemic dynamics, where nodes represent geographical areas and links connecting nodes correspond to human mobility between areas. Using a mean-field approach, we derive the theoretical spreading threshold revealing a non-trivial dependence on the heterogeneity of vaccination. Extensive Monte Carlo simulations validate the theoretical threshold and also show the complex temporal epidemic behaviours above the threshold.     

Path-space moderate deviations for a class of Curie–Weiss models with dissipation

We modify the spin-flip dynamics of the Curie–Weiss model with dissipation in Dai Pra, Fischer and Regoli (2013) by considering arbitrary transition rates and we analyze the phase-portrait as well as the dynamics of moderate fluctuations for macroscopic observables. We obtain path-space moderate deviation principles via a general analytic approach based on the convergence of non-linear generators and uniqueness of viscosity solutions for associated Hamilton–Jacobi equations. The moderate asymptotics depend crucially on the phase we are considering and, moreover, their behavior may be influenced by the choice of the rates.     

Differential capacitance of the electric double layer: mean-field modeling approaches

Electrolytes screen the charges carried by an electrode through the formation of a diffuse double layer. The corresponding differential capacitance reflects the change of the surface charge density with the applied surface potential. Mean-field modeling of the differential capacitance is an attempt to qualitatively explain experimental findings such as the camel-to-bell shape transition in terms of physical factors including the ion size and concentration, nonelectrostatic ion–ion interactions, electrostatic ion–ion correlations, and the influence of the electrode curvature. We highlight the central role of the lattice gas model as a conceptual tool to describe concentrated electrolytes and ionic liquids, and we briefly summarize how extensions and generalizations of this model give rise to concepts known as ‘overscreening’ and ‘underscreening’.