We introduce gradient flow aggregation, a random growth model. Given existing particles
$\{x_1,\ldots,x_n\} \subset \mathbb{R}^2$
, a new particle arrives from a random direction at
$\infty$
and flows in direction of the vector field
$\nabla E$
where
$ E(x) = \sum_{i=1}^{n}{1}/{\|x-x_i\|^{\alpha}}$
,
$0 < \alpha < \infty$
. The case
$\alpha = 0$
refers to the logarithmic energy
${-}\sum\log\|x-x_i\|$
. Particles stop once they are at distance 1 from 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
$0 \leq \alpha < 1$
,
$\text{diam}(\{x_1,\ldots,x_n\}) \leq c_{\alpha} \cdot n^{({3 \alpha +1})/({2\alpha + 2})}$
. 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.