A dynamical systems model of unorganized segregation

We consider Schelling’s bounded neighborhood model (BNM) of unorganized segregation, from the perspective of modern dynamical systems theory. We carry out a complete quantitative analysis of the system for linear tolerance schedules. We derive a fully predictive model and associate each term with a social meaning. We recover and generalize Schelling’s qualitative results.

For the case of unlimited population movement, we derive exact formulae for regions in parameter space where stable integrated population mixes can occur, and show how neighborhood tipping can be explained in terms of basins of attraction.

When population movement is limited, we derive exact criteria for the occurrence of new population mixes.

For nonlinear tolerance schedules, we illustrate our approach with numerical simulations.     

Non parametric estimation of the diffusion coefficients of a diffusion with jumps

In this article, we consider a jump diffusion process ${\left(X_t\right)}_{t\ge 0}$, with drift function $b$, diffusion coefficient $\sigma$ and jump coefficient ${\xi }^2$. This process is observed at discrete times $t=0,\Delta ,\dots ,n\Delta$. The sampling interval $\Delta$ tends to 0 and the time interval $n\Delta$ tends to infinity. We assume that ${\left(X_t\right)}_{t\ge 0}$ is ergodic, strictly stationary and exponentially $\beta$-mixing. We use a penalized least-square approach to compute adaptive estimators of the functions ${\sigma }^2+{\xi }^2$ and ${\sigma }^2$. We provide bounds for the risks of the two estimators.     

Nonintrusive data-based learning of a switched control heating system using POD,DMD and ANN

The aim of this work is to derive an accurate model of two-dimensional switched control heating system from data generated by a Finite Element solver. The nonintrusive approach should be able to capture both temperature fields, dynamics and the underlying switching control rule. To achieve this goal, the algorithm proposed in this paper will make use of three main ingredients: proper orthogonal decomposition (POD), dynamic mode decomposition (DMD) and artificial neural networks (ANN). Some numerical results will be presented and compared to the high-fidelity numerical solutions to demonstrate the capability of the method to reproduce the dynamics.     

A door to model reduction in high-dimensional parameter space

Model reduction techniques such as Proper Generalized Decomposition (PGD) are decision-making tools that are about to revolutionize many domains. Unfortunately, their computation is still problematic for problems involving many parameters, for which one has to face the “curse of dimensionality”. An answer to this challenge is given in solid mechanics by the so-called “parameter-multiscale PGD”, which is based on Saint-Venant's principle. In this article, a model problem composed of up to a thousand parameters is presented, showing that the method is able to overcome the “curse of dimensionality”.     

SE-MCNN-CTC的中文语音识别声学模型

针对国内外缺少对振动轮噪声预估的问题,以某型振动轮为研究对象,首先基于动力学有限元理论对振动轮进行频率响应分析,其次采用声学边界元技术对振动轮辐射噪声进行了数值模拟,并通过实验验证了仿真结果的准确性,然后比较了垂直振动与圆周振动两种不同激振形式对辐射噪声的影响,得出垂直振动辐射噪声低的结论,最后对驾驶室声腔模态进行了仿真,与振动轮激振频率相近发生共振。通过调整激振频率,降低了司机耳旁噪声。所得研究成果可为振动轮辐射噪声的预估与改进提供一种切实可行的参考依据。     

Cosmic inflation and model comparison

We analyze the implications for inflation of the recently released Planck Cosmic Microwave Background data and explain why the single-field slow-roll scenarios with minimal kinetic terms are favored. Within this class of models, we show how Bayesian model comparison can be used to further exclude about one third of the inflationary scenarios. We also study the end of inflation and show that Planck can already constrain the reheating phase. Finally, we conclude by discussing how future missions will be able to improve our knowledge of the inflationary mechanism.     

The ASBM-SA turbulence closure: Taking advantage of structure-based modeling in current engineering CFD codes

Structure-based turbulence models (SBM) carry information about the turbulence structure that is needed for the prediction of complex non-equilibrium flows. SBM have been successfully used to predict a number of canonical flows, yet their adoption rate in engineering practice has been relatively low, mainly because of their departure from standard closure formulations, which hinders easy implementation in existing codes. Here, we demonstrate the coupling between the Algebraic Structure-Based Model (ASBM) and the one-equation Spalart–Allmaras (SA) model, which provides an easy route to bringing structure information in engineering turbulence closures. As the ASBM requires correct predictions of two turbulence scales, which are not taken into account in the SA model, Bradshaw relations and numerical optimizations are used to provide the turbulent kinetic energy and dissipation rate. Attention is paid to the robustness and accuracy of the hybrid model, showing encouraging results for a number of simple test cases. An ASBM module in Fortran-90 is provided along with the present paper in order to facilitate the testing of the model by interested readers.     

直觉模糊模型检测在工程决策上的应用

将直觉模糊Kripke结构扩展到加权直觉模糊Kripke结构,将直觉模糊计算树逻辑诱导到加权直觉模糊计算树逻辑;研究在此之上的直觉模糊期望测度和多属性工程决策问题。用加权直觉模糊Kripke结构的权值自然地刻画了工程问题中的成本和收益,直觉模糊测度量化工程进展的不确定性,用加权直觉模糊计算树逻辑描述不确定性工程属性约束。给出了基于直觉模糊模型检测的多属性工程寻优算法,并讨论了算法的复杂度。     

随机模糊的等比例-最小方差投资组合优化研究

Markowitz的均值-方差模型在投资组合优化中得到了广泛的运用和拓展,其中多数拓展模型仅局限于对随机投资组合或模糊投资组合的研究,而忽略了实际问题同时包含了随机信息和模糊信息两个方面。本文首先定义随机模糊变量的方差用以度量投资组合的风险,提出具有阀值约束的最小方差随机模糊投资组合模型,基于随机模糊理论,将该模型转化为具有线性等式和不等式约束的凸二次规划问题。为了提高上述模型的有效性,本文以投资者期望效用最大化为压缩目标对投资组合权重进行压缩,构建等比例-最小方差混合的随机模糊投资组合模型,并求解该模型的最优解。最后,运用滚动实际数据的方法,比较上述两个模型的夏普比率以验证其有效性。     

A minimal model of self propelled locomotion

Fish locomotion is a complicated problem in the context of fluid–structure interaction and it is still not understood what is linked to biology and what is linked to mechanics. Measurements performed on natural fish and artificial systems reveal that swimming at high Reynolds number is found in a narrow range of Strouhal numbers - a dimensionless combination of the swimming velocity, tail beat amplitude and frequency. With a minimal model of aquatic locomotion, we investigate how this number depends on the numerous parameters at play. We show a strong correlation with the drag coefficient, while the effect of the other parameters can be neglected at the first-order approximation.