Laplacian, on the Arrowhead Curve

Claire David
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引用次数: 1

Abstract

In terms of analysis on fractals, the Sierpinski gasket stands out as one of the most studied example. The underlying aim of those studies is to determine a differential operator equivalent to the classic Laplacian. The classically adopted approach is a bidimensional one, through a sequence of so-called prefractals, i.e. a sequence of graphs that converges towards the considered domain. The Laplacian is obtained through a weak formulation, by means of Dirichlet forms, built by induction on the prefractals. It turns out that the gasket is also the image of a Peano curve, the so-called Arrowhead one, obtained by means of similarities from a starting point which is the unit line. This raises a question that appears of interest. Dirichlet forms solely depend on the topology of the domain, and not of its geometry. Which means that, if one aims at building a Laplacian on a fractal domain as the aforementioned curve, the topology of which is the same as, for instance, a line segment, one has to find a way of taking account its specific geometry. Another difference due to the geometry, is encountered may one want to build a specific measure. For memory, the sub-cells of the Kigami and Strichartz approach are triangular and closed: the similarities at stake in the building of the Curve called for semi-closed trapezoids. As far as we know, and until now, such an approach is not a common one, and does not appear in such a context. It intererestingly happens that the measure we choose corresponds, in a sense, to the natural counting measure on the curve. Also, it is in perfect accordance with the one used in the Kigami and Strichartz approach. In doing so, we make the comparison -- and the link -- between three different approaches, that enable one to obtain the Laplacian on the arrowhead curve: the natural method; the Kigami and Strichartz approach, using decimation; the Mosco approach.    
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在箭头曲线上的拉普拉斯
在分形分析方面,Sierpinski垫圈是研究最多的例子之一。这些研究的潜在目的是确定一个与经典拉普拉斯算子等价的微分算子。经典采用的方法是二维的,通过所谓的前分形序列,即向所考虑的域收敛的图序列。拉普拉斯量是通过一个弱的公式,借助于狄利克雷形式,在前分形的归纳上得到的。事实证明,垫片也是皮亚诺曲线的图像,即所谓的箭头曲线,通过从起点(即单位线)的相似性获得。这就提出了一个令人感兴趣的问题。狄利克雷形式仅仅依赖于定义域的拓扑结构,而不是它的几何形状。这意味着,如果一个人的目标是在分形域上建立一个拉普拉斯函数,如前面提到的曲线,它的拓扑结构与线段相同,他必须找到一种考虑其特定几何形状的方法。另一个由于几何形状的不同,可能会遇到一个想要构建的特定度量。对于记忆,Kigami和Strichartz方法的子细胞是三角形和封闭的:在构建曲线时的相似性被称为半封闭的梯形。据我们所知,直到现在,这样的方法并不常见,也没有出现在这样的上下文中。有趣的是,我们选择的度量,在某种意义上,与曲线上的自然计数度量相对应。此外,它与Kigami和Strichartz方法中使用的方法完全一致。在这样做的过程中,我们在三种不同的方法之间进行比较和联系,使人们能够在箭头曲线上获得拉普拉斯:自然方法;使用抽取的Kigami和Strichartz方法;莫斯科方法。
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来源期刊
Proceedings of the International Geometry Center
Proceedings of the International Geometry Center Mathematics-Geometry and Topology
CiteScore
1.00
自引率
0.00%
发文量
14
审稿时长
3 weeks
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