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arXiv 2202.09237math.AP
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二维各向异性吸引-排斥相互作用能的整体极小元

Global Minimizers of a Large Class of Anisotropic Attractive-Repulsive Interaction Energies in 2D

José A. Carrillo, Ruiwen Shu

摘要:

本文研究了一大类二维各向异性Riesz型奇异相互作用势。在一定的假设条件下,它们的全局能量极小解由显式公式给出,其支撑由椭圆确定。更确切地说,通过参数化的强度的各向异性的一部分,我们的特点是尖锐的范围内,这些明确的椭圆支持的配置是基于线性凸参数的全局极小。此外,对于某些各向异性的部分,我们证明了,对于大的参数值的全局极小值只给出了垂直集中的措施,对应于一维极小。我们还表明,这些椭圆形支持的配置一般不崩溃的凸性在临界值的垂直集中的措施,导致一个有趣的差距之间的参数。在这个中间范围内,我们通过无穷小极小值得出结论,在适当的意义下,任何局部极小元的任何超水平集都没有内点。此外,对于某些各向异性的部分,它们的支持不能包含任何垂直段的参数的限制范围内,此外,全球最小预期表现出锯齿形的行为。所有这些结果都适用于对数排斥势的极限情况,推广和推广了文献中的先前结果。各向异性零件的各种例子,导致更复杂的行为进行了数值研究。

英文摘要:

We study a large family of Riesz-type singular interaction potentials with anisotropy in two dimensions. Their associated global energy minimizers are given by explicit formulas whose supports are determined by ellipses under certain assumptions. More precisely, by parameterizing the strength of the anisotropic part we characterize the sharp range in which these explicit ellipse-supported configurations are the global minimizers based on linear convexity arguments. Moreover, for certain anisotropic parts, we prove that for large values of the parameter the global minimizer is only given by vertically concentrated measures corresponding to one dimensional minimizers. We also show that these ellipse-supported configurations generically do not collapse to a vertically concentrated measure at the critical value for convexity, leading to an interesting gap of the parameters in between. In this intermediate range, we conclude by infinitesimal concavity that any superlevel set of any local minimizer in a suitable sense does not have interior points. Furthermore, for certain anisotropic parts, their support cannot contain any vertical segment for a restricted range of parameters, and moreover the global minimizers are expected to exhibit a zigzag behavior. All these results hold for the limiting case of the logarithmic repulsive potential, extending and generalizing previous results in the literature. Various examples of anisotropic parts leading to even more complex behavior are numerically explored.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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