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arXiv 2408.05072math.APmath.PR
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有限图上分数随机游走的反问题

An inverse problem for fractional random walks on finite graphs

University of Jyväskylä(于韦斯屈莱大学);University of Helsinki(赫尔辛基大学)

Giovanni Covi, Matti Lassas

AI总结:

研究有限连通图上分数随机游走的反问题,通过部分观测确定导电率和转移矩阵的 gauge 类,揭示新的非局部性质。

摘要:

我们研究一个有限连通图 G = (X, E) 上的反问题,其顶点上定义了导电率 γ。数据由分数随机游走的部分观测组成。观测受限于固定可观测子集 B,而随机游走允许长跳,其概率 P 与距离的分数次幂成反比。转移概率 P 也依赖于 γ。我们证明此类随机游走数据可确定 P 所属的 gauge 类,并讨论其性质。此外,若 P 已知,则可恢复 |X|、E 和 γ(除正因子外)。我们还通过对应的转移矩阵 P 特征化随机游走数据,揭示新的非局部性质。此工作受最近对黎曼几何中分数 Calderón 问题研究的强烈兴趣启发。

英文摘要:

We study an inverse problem on a finite connected graph G = (X, E), on whose vertices a conductivity γ is defined. Our data consists in a sequence of partial observations of a fractional random walk on G. The observations are partial in the sense that they are limited to a fixed, observable subset B of X, while the random walk is fractional in the sense that it allows long jumps with a probability P decreasing as a fractional power of the distance along the graph. The transition probability P also depends on γ. We show that this kind of random walk data allows for the determination of a gauge class to which the transition probability matrix P belongs, which we discuss. Moreover, we show that if the transition probability matrix P is itself known, then the amount of vertices |X|, the edge set E and the conductivity γ (up to a positive factor) can be recovered. We also show a characterization of the random walk data in terms of the corresponding transition matrices P , which highlights a new surprising nonlocal property. This work is motivated by the recent strong interest in the study of the fractional Calderón problem in the Riemannian setting.

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