arXiv subjects

Katherine A. Newhall

Publications and source records attributed to Katherine A. Newhall.

13 recordsLinked to original sources

arXiv2026

Mechanism of Band Gap Formation in Beam Networks

Band gaps are commonly attributed to Bragg scattering or local resonance, yet it remains unclear whether these mechanisms govern band gap formation in beam networks. In this work, we explain band gap formation in beam networks in terms of a new mechanism, geometry-induced coupling between deformation modes. Specifically, band gap onset arises from axial-bending coupling at lattice nodes and scales with the axial cutoff frequency of a one-dimensional periodic beam, whereas band gap termination is primarily governed by high-frequency rotational branches associated with beam geometry. This mechanism holds for both periodic and disordered beam networks. In periodic lattices, it manifests through beam orientations at lattice nodes, whereas in disordered networks it manifests through short-beam statistics arising from variations in beam length. Together, these results establish a unified mechanism for band gap formation across both periodic and disordered beam networks, providing new insight into the physical origin of band gaps in beam-network materials.

cond-mat.mtrl-sci↗

arXiv2026

Effect of Local Topological Changes on Resistance in Spatially-Embedded Disordered Networks

Disordered materials occur naturally and also provide a broader design space than ordered or crystalline structures. We investigate a two-dimensional disordered network metamaterial constructed from a Delaunay triangulation of an underlying point cloud. Small perturbations in the point cloud induce discrete topological changes. One such change we identify is a Delaunay flip, in which two neighboring Delaunay triangles that form a convex quadrilateral structure with their common edge being one of the two quadrilateral diagonals exchange this diagonal for the other diagonal. These topological changes can cause substantial jumps in the effective resistance measured diagonally across the network, when the change is located near the source or the sink node. The jumps are explained analytically by showing that the change in effective resistance from edge removal or addition depends on the voltage drop across that edge. However, Delaunay flips have less impact on global resistance measurements and in larger networks. These local topological changes are relevant for finite-sized samples and experimentally-measurable properties such as electrical transport. Global characterizations of the network disorder or topology lack the location-specificity of our observed effects on network transport, and thus may be inadequate for predicting certain experimentally measurable transport properties in disordered network metamaterials, highlighting the importance of localized regions in material design.

cond-mat.dis-nn↗

arXiv2026

Probing the influence of topological and geometric disorder on the spectrum of the differential Laplacian operator on networks

Metric networks are network-shaped, one-dimensional structures on which one can solve differential equations to simulate a wide range of physical systems including conjugated molecules, photonic crystals, quantum mechanics in waveguide networks, and acoustic metamaterials. More concretely, a metric network is a network whose edges are each assigned a notion of length and a coordinate describing position. One can then define function spaces and differential operators on these objects to model the aforementioned systems. Recent software advancements have made it feasible to analyze partial differential equations on large, compact metric networks with a vast array of structures. Here, we generate compact metric network structures using the spatial tessellations of two-dimensional hyperuniform point patterns, which have suppressed large-scale density fluctuations relative to typical disordered point patterns. This choice of structure is inspired by the exotic physical properties of network materials with these structures in other contexts. Then, we characterize the eigenvalue spectrum structure of the differential Laplace operator on these networks. In particular, we find that gaps can form in the eigenvalue spectra of these networks whose widths increase when the distribution of edge lengths is narrow and as the number of triangular faces increases. Importantly, many of the structures we consider are realizable in Euclidean space, meaning they are well-suited for practical applications in, e.g., metamaterial design. This work can thus be used to inform the design of metric network-based systems with spectral gaps with tunable widths and locations.

cond-mat.dis-nn↗

arXiv2025

Local Geometric and Transport Properties of Networks that are Generated from Hyperuniform Point Patterns

Hyperuniformity, which is a type of long-range order that is characterized by the suppression of long-range density fluctuations in comparison to the fluctuations in standard disordered systems, has emerged as a powerful concept to aid in the understanding of diverse natural and engineered phenomena. In the present paper, we harness hyperuniform point patterns to generate a class of disordered, spatially embedded networks that are distinct from both perfectly ordered lattices and uniformly random geometric graphs. We refer to these networks as \emph{hyperuniform-point-pattern-induced (HuPPI) networks}, and we compare them to their counterpart \emph{Poisson-point-pattern-induced (PoPPI) networks}. By computing the local geometric and transport properties of HuPPI networks, we demonstrate how hyperuniformity imparts advantages in both transport efficiency and robustness. Specifically, we show that HuPPI networks have systematically smaller total effective resistances, slightly faster random-walk mixing times, and fewer extreme-curvature edges than PoPPI networks. Counterintuitively, we also find that HuPPI networks simultaneously have more negative mean Ollivier--Ricci curvatures and smaller total effective resistances than PoPPI networks, indicating that edges with moderately negative curvatures need not create severe bottlenecks to transport. Moreover, HuPPI networks are consistently more robust under both random edge removals and curvature-based targeted edge removals, maintaining larger connected components for larger fractions of removed edges than their PoPPI counterparts. We also demonstrate that the network-generation method strongly influences these properties and in particular that it often overshadows differences that arise from underlying point patterns.

cond-mat.dis-nn↗

arXiv2025

Comparing dragonfly wings to jars of marbles through the lens of hyperuniformity

When we look at the world around us, we see both organized (also called ordered) and disorganized (also called disordered) arrangements of things. Carefully-tiled floors and brick walls have organized and repeating patterns, but the stars in the sky and the trees in a forest look like they're arranged in a disordered way. We also see objects, like jars of marbles and the lacy wings of insects, that lie between ordered and disordered extremes. Although the marbles in a jar don't sit on a regular grid like carefully-arranged tiles, the collection of marbles does have some consistent features, such as the typical size and spacing between them. However, the positions of the marbles are much less random than the positions of the stars in the sky. To help understand and classify these patterns, mathematicians and physicists use the term hyperuniform to help them describe the situations of being perfectly organized or being disorganized in an organized way. In this article, we discuss various fascinating properties of hyperuniform patterns. We explore where they occur in the natural world and how engineers are using them to build new structures.

math.HO↗

arXiv2025

Characterizing the Hyperuniformity of Disordered Network Metamaterials

Advancements in materials design and manufacturing have allowed for the production of ordered and disordered metamaterials with diverse and novel properties. Hyperuniform two-phase heterogeneous materials, which anomalously suppress density fluctuations on large length scales compared to typical disordered systems, and network materials are two classes of metamaterial that have desirable physical properties. Recent focus has been placed on the design of disordered hyperuniform network metamaterials that inherit the desirable properties of both of these metamaterial classes. In this work, we focus on determining the extent to which network structures derived from the spatial tessellations of hyperuniform point patterns inherit the hyperuniformity of the progenitor point patterns. In particular, we examine the Delaunay, Voronoi, Delaunay-Centroidal, and Gabriel tessellations of nonhyperuniform and hyperuniform point patterns in two- and three-dimensional Euclidean space. We use the spectral density to characterize the density fluctuations of two-phase media created by thickening the edges of these tessellations in two dimensions and introduce a novel variance-based metric to characterize the network structures directly in two and three dimensions. We find that, while none of the tessellations completely inherit the hyperuniformity of the progenitor point pattern, the degree to which the hyperuniformity is inherited is sensitive to the tessellation scheme and the short- and long-range translational disorder in the point pattern, but not to the choice of beam shape when mapping the networks into two-phase media.

cond-mat.soft↗

arXiv2025

Electrical Transport in Tunably-Disordered Metamaterials

Naturally occurring materials are often disordered, with their bulk properties being challenging to predict from the structure, due to the lack of underlying crystalline axes. In this paper, we develop a digital pipeline from algorithmically-created configurations with tunable disorder to 3D printed materials, as a tool to aid in the study of such materials, using electrical resistance as a test case. The designed material begins with a random point cloud that is iteratively evolved using Lloyd's algorithm to approach uniformity, with the points being connected via a Delaunay triangulation to form a disordered network metamaterial. Utilizing laser powder bed fusion additive manufacturing with stainless steel 17-4 PH and titanium alloy Ti-6Al-4V, we are able to experimentally measure the bulk electrical resistivity of the disordered network. The effective resistance of the structure calculated from the combinatorial weighted graph Laplacian is in good agreement with experimental data. However, the effective resistance is sensitive to anisotropy and global network topology, preventing a single network statistic or disorder characterization from predicting global resistivity.

cond-mat.dis-nn↗

arXiv2022

Self-avoidant memory effects on enhanced diffusion in a stochastic model of environmentally responsive swimming droplets

Enhanced diffusion is an emergent property of many experimental microswimmer systems that usually arises from a combination of ballistic motion with random reorientations. A subset of these systems, autophoretic droplet swimmers that move as a result of Marangoni stresses, have additionally been shown to respond to local, self-produced chemical gradients that can mediate self-avoidance or self-attraction. Via this mechanism, we present a mathematical model constructed to encode experimentally observed self-avoidant memory and numerically study the effect of this particular memory on the enhanced diffusion of such swimming droplets. To disentangle the enhanced diffusion due to the random reorientations from the enhanced diffusion due to the self-avoidant memory, we compare to the widely-used active Brownian model. Paradoxically, we find that the enhanced diffusion is substantially suppressed by the self-avoidant memory relative to that predicted by only an equivalent reorientation persistence timescale in the active Brownian model. We attribute this to transient self-caging that we propose is novel for self-avoidant systems. Additionally, we further explore the model parameter space by computing emergent parameters that capture the velocity and reorientation persistence, thus finding a finite parameter domain in which enhanced diffusion is observable.

cond-mat.soft↗

arXiv2021

The Hydra String Method: A Novel Means to Explore Potential Energy Surfaces and its Application to Granular Materials

Granular materials are a ubiquitous yet ill-understood class of media. Many different approaches and techniques have been developed to understand the many complex behaviors they exhibit but none have been completely successful. We present a novel means to understand granular materials, the Hydra String Method (HSM). This is an efficient and autonomous way to trawl an arbitrary potential energy surface (or any similarly high dimensional function) that enumerates the saddle points, minima, and minimum energy paths between them. In doing so, it creates a reduced dimensional network representation of this function. We also present a series of tests to choose optimized parameters for the application of the HSM. We apply this to the potential energy function of a granular system consisting of a bidisperse configuration of frictionless soft spheres. Future work will make use of the found ensemble of transition pathways to statistically predict the dynamics of a system of grains.

cond-mat.soft↗

arXiv2020

Non-local SPDE limits of spatially-correlated-noise driven spin systems derived to sample a canonical distribution

We study the macroscopic behavior of a stochastic spin ensemble driven by a discrete Markov jump process motivated by the Metropolis-Hastings algorithm where the proposal is made with spatially correlated (colored) noise, and hence fails to be symmetric. However, we demonstrate a scenario where the failure of proposal symmetry is a higher order effect. Hence, from these microscopic dynamics we derive as a limit as the proposal size goes to zero and the number of spins to infinity, a non-local stochastic version of the harmonic map heat flow (or overdamped Landau-Lipshitz equation). The equation is both mathematically well-posed and samples the canonical/Gibbs distribution related to the kinetic energy. The failure of proposal symmetry due to interaction between the confining geometry of the spin system and the colored noise is in contrast to the uncorrelated, white-noise, driven system. Specifically, the choice of projection of the noise to conserve the magnitude of the spins is crucial to maintaining the proper equilibrium distribution. Numerical simulations are included to verify convergence properties and demonstrate the dynamics.

cond-mat.stat-mech↗

arXiv2015

The Structure of Global Attractors for Dissipative Zakharov Systems with Forcing on the Torus

The Zakharov system was originally proposed to study the propagation of Langmuir waves in an ionized plasma. In this paper, motivated by earlier work of the first and third authors, we numerically and analytically investigate the dynamics of the dissipative Zakharov system on the torus in 1 dimension. We find an interesting family of stable periodic orbits and fixed points, and explore bifurcations of those points as we take weaker and weaker dissipation.

math.AP↗

arXiv2013

Random polarization dynamics in a resonant optical medium

Random optical-pulse polarization switching along an active optical medium in the $Λ$-configuration with spatially disordered occupation numbers of its lower energy sub-level pair is described using the idealized integrable Maxwell-Bloch model. Analytical results describing the light polarization-switching statistics for the single self-induced transparency pulse are compared with statistics obtained from direct Monte-Carlo numerical simulations.

physics.optics↗

arXiv2012

A granocentric model captures the statistical properties of monodisperse random packings

We present a generalization of the granocentric model proposed in [Clusel et al., Nature, 2009, 460, 611615] that is capable of describing the local fluctuations inside not only polydisperse but also monodisperse packings of spheres. This minimal model does not take into account the relative particle positions, yet it captures positional disorder through local stochastic processes sampled by efficient Monte Carlo methods. The disorder is characterized by the distributions of local parameters, such as the number of neighbors and contacts, filled solid angle around a central particle and the cell volumes. The model predictions are in good agreement with our experimental data on monodisperse random close packings of PMMA particles. Moreover, the model can be used to predict the distributions of local fluctuations in any packing, as long as the average number of neighbors, contacts and the packing fraction are known. These distributions give a microscopic foundation to the statistical mechanics framework for jammed matter and allow us to calculate thermodynamic quantities such as the compactivity in the phase space of possible jammed configurations.

physics.bio-ph↗