The Almost Sure Asymptotic Stability and Boundedness of Stochastic Functional Differential Equations with Polynomial Growth Condition

Stability and boundedness are two of the most important topics in the study of stochastic functional differential equations (SFDEs). This paper mainly discusses the almost sure asymptotic stability and the boundedness of nonlinear SFDEs satisfying the local Lipschitz condition but not the linear growth condition. Here we assume that the coefficients of SFDEs are polynomial or dominated by polynomial functions. We give sufficient criteria on the almost sure asymptotic stability and the boundedness for this kind of nonlinear SFDEs. Some nontrivial examples are provided to illustrate our results

Combining clusterings in the belief function framework

International audienceIn this paper, we propose a clustering ensemble method based on Dempster-Shafer Theory. In the first step, base partitions are generated by evidential clustering algorithms such as the evidential c-means or EVCLUS. Base credal partitions are then converted to their relational representations, which are combined by averaging. The combined relational representation is then made transitive using the theory of intuitionistic fuzzy relations. Finally, the consensus solution is obtained by minimizing an error function. Experiments with simulated and real datasets show the good performances of this method

Asymptotic stability and boundedness of stochastic functional differential equations with Markovian switching

This paper is concerned with the boundedness, exponential stability and almost sure asymptotic stability of stochastic functional differential equations (SFDEs) with Markovian switching. The key technique used is the method of multiple Lyapunov functions. We use two auxiliary functions to dominate the corresponding different Lyapunov function in different mode while the diffusion operator in different model is controlled by other multiple auxiliary functions. Our conditions on the diffusion operator are weaker than those in the related existing works

Approximate Solutions of Set-Valued Stochastic Differential Equations

Abstract In this paper, we consider the problem of approximate solutions of set-valued stochastic differential equations. We firstly prove an inequality of set-valued Itô integrals, which is related to classical Itô isometry, and an inequality of set-valued Lebesgue integrals. Both of the inequalities play an important role to discuss set-valued stochastic differential equations. Then we mainly state the Carathodory's approximate method and the Euler-Maruyama's approximate method for set-valued stochastic differential equations. We also investigate the errors between approximate solutions and accurate solutions

3D phase field modeling of multi-dendrites evolution in solidification and validation by synchrotron x-ray tomography

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. In this paper, the dynamics of multi-dendrite concurrent growth and coarsening of an Al-15 wt.% Cu alloy was studied using a highly computationally efficient 3D phase field model and real-time synchrotron X-ray micro-tomography. High fidelity multi-dendrite simulations were achieved and the results were compared directly with the time-evolved tomography datasets to quantify the relative importance of multi-dendritic growth and coarsening. Coarsening mechanisms under different solidification conditions were further elucidated. The dominant coarsening mechanisms change from small arm melting and interdendritic groove advancement to coalescence when the solid volume fraction approaches ~0.70. Both tomography experiments and phase field simulations indicated that multi-dendrite coarsening obeys the classical Lifshitz–Slyozov–Wagner theory Rn − Rn0=kc(t − t0), but with a higher constant of n = 4.3