论文标题
2D中一大型各向异性吸引力 - 抑制互动能的全球最小化器
Global Minimizers of a Large Class of Anisotropic Attractive-Repulsive Interaction Energies in 2D
论文作者
论文摘要
我们研究了一大批Riesz型奇异相互作用势与各向异性的二维。它们相关的全球能量最小化是由明确的公式给出的,其支撑是在某些假设下由椭圆确定的。更准确地说,通过参数化各向异性部分的强度,我们表征了这些显式椭圆支撑的配置的尖锐范围,是基于线性凸参数的全局最小化器。此外,对于某些各向异性部分,我们证明,对于大量参数值,全局最小化器仅由对应于一个维度最小化器的垂直浓缩度量给出。我们还表明,这些椭圆支撑的配置通常不会塌陷为垂直集中的量度,以凸的临界值,从而导致介于两者之间的参数的有趣差距。在这个中间范围内,我们以无穷小的凹度得出结论,任何局部最小化器在适当意义上都没有内部点。此外,对于某些各向异性部分,它们的支撑不能包含有限范围的参数范围的任何垂直段,此外,预计全局最小化器将表现出锯齿形的行为。所有这些结果都符合对数排斥潜力的限制情况,从而扩展了文献中先前的结果。在数值上探索了导致更复杂行为的各向异性部分的各种示例。
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.