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Jonathan C. Mattingly

Publications and source records attributed to Jonathan C. Mattingly.

At least 19 recordsLinked to original sources

The Batchelor spectrum for a deterministically driven passive scalar

We study the long-time behavior of a passive scalar transported by an incompressible flow in the presence of smooth, deterministic forcing. For a specific spatially Lipschitz and time-periodic velocity field, we prove that all sufficiently smooth initial data is attracted to a limiting solution that satisfies a cumulative form of Batchelor's law. To our knowledge, this provides the first example for which a version of Batchelor's law can be established with deterministic forcing.

math.AP

Discrete Bound States in a Toy Model for Weak Turbulence and Implications for the Invariant Measure

A model Hamiltonian dynamical system has been derived to study frequency cascades in the cubic defocusing nonlinear Schr\"odinger equation on the torus. Here, we explore the framework for exploring a canonical ensemble formulation of the dynamics through classification of energy minimizers for fixed mass and characterizing the invariant measure in a neighborhood of those minimizers.

math.CA

A Cycle Walk for Sampling Measures on Spanning Forests for Redistricting

We introduce the Cycle Walk, a new Markov chain Monte Carlo method for sampling distributions on balanced graph partitions, motivated by applications in political redistricting. The method operates on spanning forests and combines two types of updates: local "cycle" moves within districts and global moves that exchange population between adjacent districts while preserving balance constraints. This construction enables efficient Metropolis--Hastings correction while allowing proposals at multiple spatial scales. We show that the Cycle Walk naturally interpolates between existing approaches based on local updates and a class of global update methods derived from recombination (RECOM). Through a range of numerical experiments on synthetic graphs and real-world precinct data, we demonstrate that the Cycle Walk exhibits improved empirical convergence diagnostics for distributions that place weaker weight on spanning-tree counts, a regime that is challenging for existing methods. In particular, the algorithm remains effective when incorporating alternative compactness measures that more closely reflect policy-relevant criteria. These results suggest that the Cycle Walk provides a flexible and computationally efficient framework for sampling from a broader class of redistricting distributions than previously accessible with MCMC techniques.

cs.SI

A pathwise approach to the enhanced dissipation of passive scalars advected by shear flows

We develop a framework for studying the enhanced dissipation of passive scalars advected by shear flows based on analyzing the particle trajectories of the stochastic differential equation associated with the governing drift-diffusion equation. We consider both shear flows on $\mathbb{T}^2$ and radially symmetric shears on $\mathbb{R}^2$ or the unit disk. Using our probabilistic approach, we are able to recover the well-known enhanced dissipation timescale for smooth shear flows on $\mathbb{T}^2$ with finite-order vanishing critical points [1, 5, 34, 36] and a generalized version of the results for radially symmetric shear flows from [42]. We also obtain results for shear flows with singularities and critical points where the derivative vanishes to infinite order. The proofs are all based on using Girsanov's theorem to reduce enhanced dissipation to a quantitative control problem that can be solved by leveraging the shearing across streamlines. Our method also has the feature that it is local in space, which allows us also to obtain estimates on the precise decay rate of solutions along each streamline in terms of the local shear profile.

math.AP

Phase space contraction of degenerately damped random splittings

When studying out-of-equilibrium systems, one often excites the dynamics in some degrees of freedom while removing the excitation in others through damping. In order for the system to converge to a statistical steady state, the dynamics must transfer the energy from the excited modes to the dissipative directions. The precise mechanisms underlying this transfer are of particular interest and are the topic of this paper. We explore a class of randomly switched models introduced in [2,3] and provide some of the first results showing that minimal damping is sufficient to stabilize the system in a fluids model.

math.PR

Multiscale Parallel Tempering for Fast Sampling on Redistricting Plans

When auditing a redistricting plan, a persuasive method is to compare the plan with an ensemble of neutrally drawn redistricting plans. Ensembles are generated via algorithms that sample distributions on balanced graph partitions. To audit the partisan difference between the ensemble and a given plan, one must ensure that the non-partisan criteria are matched so that we may conclude that partisan differences come from bias rather than, for example, levels of compactness or differences in community preservation. Certain sampling algorithms allow one to explicitly state the policy-based probability distribution on plans, however, these algorithms have shown poor mixing times for large graphs (i.e. redistricting spaces) for all but a few specialized measures. In this work, we generate a multiscale parallel tempering approach that makes local moves at each scale. The local moves allow us to adopt a wide variety of policy-based measures. We examine our method in the state of Connecticut and succeed at achieving fast mixing on a policy-based distribution that has never before been sampled at this scale. Our algorithm shows promise to expand to a significantly wider class of measures that will (i) allow for more principled and situation-based comparisons and (ii) probe for the typical partisan impact that policy can have on redistricting.

physics.soc-ph

Random Splitting of Fluid Models: Positive Lyapunov Exponents

In this paper we give sufficient conditions for random splitting systems to have a positive top Lyapunov exponent. We verify these conditions for random splittings of two fluid models: the conservative Lorenz-96 equations and Galerkin approximations of the 2D Euler equations on the torus. In doing so, we highlight particular structures in these equations such as shearing. Since a positive top Lyapunov exponent is an indicator of chaos which in turn is a feature of turbulence, our results show these randomly split fluid models have important characteristics of turbulent flow.

math.DS

Random Splitting of Point Vortex Flows

We consider a stochastic version of the point vortex system, in which the fluid velocity advects single vortices intermittently for small random times. Such system converges to the deterministic point vortex dynamics as the rate at which single components of the vector field are randomly switched diverges, and therefore it provides an alternative discretization of 2D Euler equations. The random vortex system we introduce preserves microcanonical statistical ensembles of the point vortex system, hence constituting a simpler alternative to the latter in the statistical mechanics approach to 2D turbulence.

math.PR

Shadow geometry at singular points of CAT(k) spaces

In any CAT(k) space M, the "shadow" of a tangent vector Z at a point p is the set vectors that form an angle of πor more with Z. Taking logarithm maps at points approaching p along a fixed geodesic ray from p with tangent Z collapses the shadow to a single ray while leaving isometrically intact every convex cone that avoids the shadow of Z.

math.MG

Geometry of measures on smoothly stratified metric spaces

Any measure $μ$ on a CAT(k) space M that is stratified as a finite union of manifolds and has local exponential maps near the Fréchet mean $\barμ$ yields a continuous "tangential collapse" from the tangent cone of M at $\barμ$ to a vector space that preserves the Fréchet mean, restricts to an isometry on the "fluctuating cone" of directions in which the Fréchet mean can vary under perturbation of $μ$, and preserves angles between arbitrary and fluctuating tangent vectors at the Fréchet mean.

math.MG

A central limit theorem for random tangent fields on stratified spaces

Variation of empirical Fr\'echet means on a metric space with curvature bounded above is encoded via random fields indexed by unit tangent vectors. A central limit theorem shows these random tangent fields converge to a Gaussian such field and lays the foundation for more traditionally formulated central limit theorems in subsequent work.

math.PR

Central limit theorems for Fréchet means on stratified spaces

Fréchet means of samples from a probability measure $μ$ on any smoothly stratified metric space M with curvature bounded above are shown to satisfy a central limit theorem (CLT). The methods and results proceed by introducing and proving analytic properties of the "escape vector" of any finitely supported measure $δ$ in M, which records infinitesimal variation of the Fréchet mean $\barμ$ of $μ$ in response to perturbation of $μ$ by adding the mass $tδ$ for $t \to 0$. The CLT limiting distribution $N$ on the tangent cone $T$ at the Fréchet mean is characterized in four ways. The first uses tangential collapse $L$ to compare $T$ with a linear space and then applies a distortion map to the usual linear CLT to transfer back to $T$. Distortion is defined by applying escape after taking preimages under $L$. The second characterization constructs singular analogues of Gaussian measures on smoothly stratified spaces and expresses $N$ as the escape vector of any such "Gaussian mass". The third characterization expresses $N$ as the directional derivative, in the space of measures on $M$, of the barycenter map at $μ$ in the (random) direction given by any Gaussian mass. The final characterization expresses $N$ as the directional derivative, in the space $C$ of continuous real-valued functions on $T$, of a minimizer map, with the derivative taken at the Fréchet function $F \in C$ along the (random) direction given by the negative of the Gaussian tangent field induced by $μ$. Precise mild hypotheses on the measure $μ$ guarantee these CLTs, whose convergence is proved via the second characterization of $N$ by formulating a duality between Gaussian masses and Gaussian tangent fields.

math.PR

Optimal enhanced dissipation and mixing for a time-periodic, Lipschitz velocity field on $\mathbb{T}^2$

We consider the advection-diffusion equation on $\mathbb{T}^2$ with a Lipschitz and time-periodic velocity field that alternates between two piecewise linear shear flows. We prove enhanced dissipation on the timescale $|\log ν|$, where $ν$ is the diffusivity parameter. This is the optimal decay rate as $ν\to 0$ for uniformly-in-time Lipschitz velocity fields. We also establish exponential mixing for the $ν= 0$ problem.

math.AP

Gibbsian dynamics and the generalized Langevin equation

We study the statistically invariant structures of the nonlinear generalized Langevin equation (GLE) with a power-law memory kernel. For a broad class of memory kernels, including those in the subdiffusive regime, we construct solutions of the GLE using a Gibbsian framework, which does not rely on existing Markovian approximations. Moreover, we provide conditions on the decay of the memory to ensure uniqueness of statistically steady states, generalizing previous known results for the GLE under particular kernels as a sum of exponentials.

math.PR

Noise-induced strong stabilization

We consider a 2-dimensional stochastic differential equation in polar coordinates depending on several parameters. We show that if these parameters belong to a specific regime then the deterministic system explodes in finite time, but the random dynamical system corresponding to the stochastic equation is not only strongly complete but even admits a random attractor.

math.DS

Random Splitting of Fluid Models: Ergodicity and Convergence

We introduce a family of stochastic models motivated by the study of nonequilibrium steady states of fluid equations. These models decompose the deterministic dynamics of interest into fundamental building blocks, i.e., minimal vector fields preserving some fundamental aspects of the original dynamics. Randomness is injected by sequentially following each vector field for a random amount of time. We show under general assumptions that these random dynamics possess a unique invariant measure and converge almost surely to the original, deterministic model in the small noise limit. We apply our construction to the Lorenz-96 equations, often used in studies of chaos and data assimilation, and Galerkin approximations of the 2D Euler and Navier-Stokes equations. An interesting feature of the models developed is that they apply directly to the conservative dynamics and not just those with excitation and dissipation.

math.PR

Metropolized Forest Recombination for Monte Carlo Sampling of Graph Partitions

We develop a new Markov chain on graph partitions that makes relatively global moves yet is computationally feasible to be used as the proposal in the Metropolis-Hastings method. Our resulting algorithm can be made reversible and able to sample from a specified measure on partitions. Both of these properties are critical to some important applications and computational Bayesian statistics in general. Our proposal chain modifies the recently developed method called Recombination (ReCom), which draws spanning trees on joined partitions and then randomly cuts them to repartition. We improve the computational efficiency by augmenting the state space from partitions to spanning forests. The extra information accelerates the computation of the forward and backward proposal probabilities. We demonstrate this method by sampling redistricting plans and find promising convergence results on several key observables of interest.

cs.DS

The Gaussian Structure of the Singular Stochastic Burgers Equation

We consider the stochastically forced Burgers equation with an emphasis on spatially rough driving noise. We show that the law of the process at a fixed time $t$, conditioned on no explosions, is absolutely continuous with respect to the stochastic heat equation obtained by removing the nonlinearity from the equation. This establishes a form of ellipticity in this infinite dimensional setting. The results follow from a recasting of the Girsanov Theorem to handle less spatially regular solutions while only proving absolute continuity at a fixed time and not on path-space. The results are proven by decomposing the solution into the sum of auxiliary processes which are then shown to be absolutely continuous in law to a stochastic heat equation. The number of levels in this decomposition diverges to infinite as we move to the stochastically forced Burgers equation associated to the KPZ equation, which we conjecture is just beyond the validity of our results (and certainly the current proof). The analysis provides insights into the structure of the solution as we approach the regularity of KPZ. A number of techniques from singular SPDEs are employed as we are beyond the regime of classical solutions for much of the paper.

math.PR