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We apply analytical and numerical methods to study the linear stability of stripe patterns in two generalizations of the two-dimensional Swift-Hohenberg equation that include coupling to a mean flow. A projection operator is included in our…

Dynamical Systems · Mathematics 2019-10-03 J. A. Weliwita , A. M. Rucklidge , S. M. Tobias

The asymmetric skew divergence smooths one of the distributions by mixing it, to a degree determined by the parameter $\lambda$, with the other distribution. Such divergence is an approximation of the KL divergence that does not require the…

Information Theory · Computer Science 2021-04-27 Masanari Kimura , Hideitsu Hino

We present numerical studies of wetting on various topographic substrates, including random topographies. We find good agreement with recent predictions based on an analytical interface-displacement-type theory \cite{Herminghaus2012,…

Fluid Dynamics · Physics 2016-03-23 Renaud Dufour , Ciro Semprebon , Stephan Herminghaus

The rheology of surface granular flows is investigated by means of measurements of velocity and number density profiles in a quasi-two-dimensional rotating cylinder, half-filled with mono-disperse steel balls. The measurements are made at…

Soft Condensed Matter · Physics 2015-06-25 Ashish V. Orpe , D. V. Khakhar

Interval exchange maps are related to geodesic flows on translation surfaces; they correspond to the first return maps of the vertical flow on a transverse segment. The Rauzy-Veech induction on the space of interval exchange maps provides a…

Geometric Topology · Mathematics 2008-01-28 Corentin Boissy , Erwan Lanneau

The flow of viscous fluids is considered as the aggregation of the motion of fluid particles when the fluid is conceived to be made up by an infinite number of particles. As an alternative of this conventional model, fluid motion could be…

Fluid Dynamics · Physics 2024-02-07 Wennan Zou , Jian He

During the wear process of surfaces in sliding friction, there is a running-in period during which the topography of surfaces changes with time before reaching the steady wear regime. In the steady wear regime, the statistical parameters…

Classical Physics · Physics 2025-03-31 Noël Brunetière

We consider smooth flows preserving a smooth invariant measure, or, equivalently, locally Hamiltonian flows on compact orientable surfaces and show that, when the genus of the surface is two, almost every such locally Hamiltonian flow with…

Dynamical Systems · Mathematics 2020-12-30 Jon Chaika , Krzysztof Frączek , Adam Kanigowski , Corinna Ulcigrai

We prove that square-tiled surfaces having fixed combinatorics of horizontal cylinder decomposition and tiled with smaller and smaller squares become asymptotically equidistributed in any ambient linear $GL(\mathbb R)$-invariant suborbifold…

Geometric Topology · Mathematics 2019-03-28 Vincent Delecroix , Elise Goujard , Peter Zograf , Anton Zorich

Fully packed loop models describe the statistics of closely packed nested polygons on the square lattice. Many exact results can be obtained for these models, even for finite geometries, using their close relationship to alternating-sign…

Statistical Mechanics · Physics 2009-01-27 Jan de Gier

We develop a one-dimensional network model to predict the steady-state distribution of yield-stress fluids in branched pipe manifolds under wall-slip conditions. The model accounts for major friction losses between junctions and…

Fluid Dynamics · Physics 2025-11-18 Elliott Sutton , Waldo Rosales Trujillo , Adam Kowalski , Cláudio P. Fonte , Anne Juel

Let $(M,g)$ be a smooth compact Riemannian surface with no boundary. Given a smooth vector field $V$ with finitely many zeroes on $M$, we study the distribution of the number of tangencies to $V$ of the nodal components of random…

Probability · Mathematics 2020-06-23 Suresh Eswarathasan , Igor Wigman

It has repeatedly been observed that loss minimization by stochastic gradient descent (SGD) leads to heavy-tailed distributions of neural network parameters. Here, we analyze a continuous diffusion approximation of SGD, called homogenized…

Machine Learning · Statistics 2024-02-05 Zhe Jiao , Martin Keller-Ressel

Stochastic Gradient Descent (SGD) based methods have been widely used for training large-scale machine learning models that also generalize well in practice. Several explanations have been offered for this generalization performance, a…

Machine Learning · Computer Science 2021-02-11 Yikai Zhang , Wenjia Zhang , Sammy Bald , Vamsi Pingali , Chao Chen , Mayank Goswami

Steady statistics of a passive scalar advected by a random two-dimensional flow of an incompressible fluid is described in the range of scales between the correlation length of the flow and the diffusion scale. That corresponds to the…

Condensed Matter · Physics 2009-10-22 M. Chertkov , G. Falkovich , I. Kolokolov , V. Lebedev

We study theoretically the surface response of a semi-infinite viscoelastic polymer network using the two-fluid model. We focus on the overdamped limit and on the effect of the network's intrinsic length scales. We calculate the decay rate…

Soft Condensed Matter · Physics 2020-05-22 Chen Bar-Haim , Haim Diamant

Many real transportation and mobility networks have their vertices placed on the surface of the Earth. In such embeddings, the edges laid on that surface may cross. In his pioneering research, Moon analyzed the distribution of the number of…

Discrete Mathematics · Computer Science 2020-08-12 Lluís Alemany-Puig , Mercè Mora , Ramon Ferrer-i-Cancho

Skew scattering is the well-known dominant mechanism for anomalous Hall transport in highly conductive systems. However, despite extensive research, the primary mechanism governing nonlinear (nonreciprocal) magneto-transport in clean…

Mesoscale and Nanoscale Physics · Physics 2025-05-14 Cong Xiao , Yue-Xin Huang , Shengyuan A. Yang

Direct numerical simulations are conducted to study the receptivity and transition mechanisms in a solitary wave boundary layer developing over randomly organized wave-like bottom topography. The boundary layer flow shows a selective…

Fluid Dynamics · Physics 2021-02-24 Asim Önder , Philip L. -F. Liu

The success of deep neural networks hinges on our ability to accurately and efficiently optimize high-dimensional, non-convex functions. In this paper, we empirically investigate the loss functions of state-of-the-art networks, and how…

Machine Learning · Computer Science 2017-12-11 Daniel Jiwoong Im , Michael Tao , Kristin Branson