Random Growth via Gradient Flow Aggregation

Abstract

We introduce Gradient Flow Aggregation (GFA), a random growth model. Given a set of existing particles $\left\{x_1,\dots ,x_n\right\}\subset {\mathbb{R}}^2$, a new particle arrives from a random direction at $\infty$ and flows in direction $\nabla E$ where $$ E(x) = \sum_{i=1}^{n} \frac{1}{\|x-x_i\|^α} \qquad \mbox{where} ~0 < α< \infty.$$ The case $\alpha =0$ will refer to the logarithmic energy $-\sum \log \Vert x-x_i\Vert$. Particles stop once they are at distance 1 of one of the existing particles at which point they are added to the set and remain fixed for all time. We prove, under a non-degeneracy assumption, a Beurling-type estimate which, via Kesten's method, can be used to deduce sub-ballistic growth for $0\le \alpha <1$ $$\mbox{diam}(\left\{x_1, \dots, x_n\right\}) \leq c_α \cdot n^{\frac{3 α+1}{2α+ 2}}.$$ This is optimal when $\alpha =0$. The case $\alpha =0$ leads to a `round' full-dimensional tree. The larger the value of $\alpha$ the sparser the tree. Some instances of the higher-dimensional setting are also discussed.

References in corpus (3)

• Scaling limits for planar aggregation with subcritical fluctuations
• Upper bounds on the growth rate of Diffusion Limited Aggregation
• Growth of Stationary Hastings-Levitov