arXiv subjects

Ethan Dudley

Publications and source records attributed to Ethan Dudley.

3 recordsLinked to original sources

arXiv2025

The Existence of Anomalous Dissipation over Bounded Interior Domains

A prevalent feature of three-dimensional turbulence is the presence of anomalous dissipation, or that the mean rate of energy dissipation is bounded below by a positive number in the inviscid limit. This is thought to be due to the nonlinear convection term in the Navier Stokes equations stretching vortex tubules and thereby increasing the amount of small scale oscillations within the flow. In this paper, we construct an example of a linear Stokes flow within a sphere that exhibits anomalous dissipation.

math.AP↗

arXiv2023

Necessary and Sufficient Conditions for Kolmogorov's Flux Laws on $\mathbb{T}^2$ and $\mathbb{T}^3$

Necessary and sufficient conditions for the third order Kolmogorov universal scaling flux laws are derived for the stochastically forced incompressible Navier-Stokes equations on the torus in 2d and 3d. This paper rigorously generalizes the result of \cite{bedrossian2019sufficient} to functions which are heavy-tailed in Fourier space or have local finite time singularities in the inviscid limit. In other words we have rigorously derived the well known physical relationship the direct cascade is a local process and is non-trivial if and only if energy moves toward the small scales or singularities have occurred. Similarly, an inverse cascade occurs if and only if energy moves towards the $k = 0$ Fourier mode in the invisicid limit.

math.AP↗

arXiv2020

Monte Carlo Estimators for the Schatten p-norm of Symmetric Positive Semidefinite Matrices

We present numerical methods for computing the Schatten $p$-norm of positive semi-definite matrices. Our motivation stems from uncertainty quantification and optimal experimental design for inverse problems, where the Schatten $p$-norm defines a design criterion known as the P-optimal criterion. Computing the Schatten $p$-norm of high-dimensional matrices is computationally expensive. We propose a matrix-free method to estimate the Schatten $p$-norm using a Monte Carlo estimator and derive convergence results and error estimates for the estimator. To efficiently compute the Schatten $p$-norm for non-integer and large values of $p$, we use an estimator using a Chebyshev polynomial approximation and extend our convergence and error analysis to this setting as well. We demonstrate the performance of our proposed estimators on several test matrices and through an application to optimal experimental design of a model inverse problem.

math.NA↗