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Douglas Barnes

Publications and source records attributed to Douglas Barnes.

4 recordsLinked to original sources

The Bardos-Tartar Conjecture for the Navier-Stokes Equations

We consider the incompressible viscous Navier-Stokes equations in dimension $d=2,3$ with periodic boundary conditions and time-independent external forcing. When $d=2$, the Bardos-Tartar conjecture asks whether the set of solutions at any given positive time is dense in the Hilbert subspace of $L^2$ consisting of mean-zero divergence-free vector fields, with respect to the strong $L^2$ norm. We prove the Bardos-Tartar conjecture and, more generally, its analogue for the hyperviscous Navier-Stokes equations studied by Guo-Titi. In addition, for $d=2,3$ we show that any given initial condition is either backwards-extendable (i) for all values of viscosity $ν>0$, or (ii) only for viscosities that belong to a Lebesgue-null subset of $\mathbb{R}_+$. When (i) holds, we show the initial condition and forcing must be real-analytic (in fact, both belong to $D(e^{σA})$ for some $σ>0$, where $A$ is the Stokes operator). Furthermore, we show that (i) is extremely unstable under perturbations. In particular, given any initial condition we show that two Fourier modes can be perturbed so that (ii) holds.

math.AP↗

Cubes in the Torus

For $q> p$, let $T(n,q,p)$ be the minimum number of translates of the cube \(\{0,1,\dots,p-1\}^n\) required to cover the $n$-dimensional torus $(\mathbb{Z}/q\mathbb{Z})^n$. We show that for each $q$ there exists a constant $1\le Λ_q \le 2$ such that $T(n,q,2)=(Λ_q + o(1))(q/2)^n$.

math.CO↗

On low frequency inference for diffusions without the hot spots conjecture

We remove the dependence on the `hot-spots' conjecture in two of the main theorems of the recent paper of Nickl (2024, Annals of Statistics). Specifically, we characterise the minimax convergence rates for estimation of the transition operator $P_{f}$ arising from the Neumann Laplacian with diffusion coefficient $f$ on arbitrary convex domains with smooth boundary, and further show that a general Lipschitz stability estimate holds for the inverse map $P_f\mapsto f$ from $H^2\to H^2$ to $L^1$.

math.ST↗

Reconstructing almost all of a point set in $\mathbb{R}^d$ from randomly revealed pairwise distances

Let $V$ be a set of $n$ points in $\mathbb{R}^d$, and suppose that the distance between each pair of points is revealed independently with probability $p$. We study when this information is sufficient to reconstruct large subsets of $V$, up to isometry. Strong results for $d=1$ have been obtained by Girão, Illingworth, Michel, Powierski, and Scott. In this paper, we investigate higher dimensions, and show that if $p>n^{-2/(d+4)}$, then we can reconstruct almost all of $V$ up to isometry, with high probability. We do this by relating it to a polluted graph bootstrap percolation result, for which we adapt the methods of Balogh, Bollobás, and Morris.

math.CO↗