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In this paper, we study an ordinary differential equation with a degenerate global attractor at the origin, to which we add a white noise with a small parameter that regulates its intensity. Under general conditions, for any fixed…

Probability · Mathematics 2025-05-27 Gerardo Barrera , Conrado da Costa , Milton Jara

We study the phenomenon of jamming in driven diffusive systems. We introduce a simple microscopic model in which jamming of a conserved driven species is mediated by the presence of a non-conserved quantity, causing an effective long range…

Statistical Mechanics · Physics 2009-10-30 O. J. O'Loan , M. R. Evans , M. E. Cates

We study "random surfaces," which are random real (or integer) valued functions on Z^d. The laws are determined by convex, nearest neighbor, difference potentials that are invariant under translation by a full-rank sublattice L of Z^d; they…

Probability · Mathematics 2007-05-23 Scott Sheffield

A one-dimensional long-range model of classical rotators with an extended degree of complexity, as compared to paradigmatic long-range systems, is introduced and studied. Working at constant density, in the thermodynamic limit one can prove…

Statistical Mechanics · Physics 2015-09-03 Alessio Turchi , Duccio Fanelli , Xavier Leoncini

The geometric dimensionality of a physical system significantly impacts its fundamental characteristics. While experiments are fundamentally limited to the maximum of three spatial dimensions, there is a growing interest in harnessing…

Many electronic systems exhibit striking features in their dynamical response over a prominent range of experimental parameters. While there are empirical suggestions of particular increasing length scales that accompany such transitions,…

Statistical Mechanics · Physics 2015-04-02 Zohar Nussinov , Patrick Johnson , Matthias J. Graf , Alexander V. Balatsky

We construct a kinetic model for matter-radiation interactions whose hydrodynamic gradient expansion can be computed analytically up to infinite order in derivatives, in the fully nonlinear regime, and for arbitrary flows. The frequency…

Nuclear Theory · Physics 2024-07-18 Lorenzo Gavassino

The pressure and the viscosity in two-dimensional sheared granular assemblies are investigated numerically. The behavior of both pressure and viscosity is smoothly changing qualitatively when starting from a mono-disperse hard-disk system…

Statistical Mechanics · Physics 2010-07-20 Michio Otsuki , Hisao Hayakawa , Stefan Luding

We study the convergence properties of gradient descent for training deep linear neural networks, i.e., deep matrix factorizations, by extending a previous analysis for the related gradient flow. We show that under suitable conditions on…

Machine Learning · Computer Science 2021-11-25 Gabin Maxime Nguegnang , Holger Rauhut , Ulrich Terstiege

We study the asymptotic behaviour of a gradient system in a regime in which the driving energy becomes singular. For this system gradient-system convergence concepts are ineffective. We characterize the limiting behaviour in a different…

Analysis of PDEs · Mathematics 2021-11-17 Mark A. Peletier , Mikola C. Schlottke

Jamming transitions and the rheology of granular avalanches in fluids are investigated using experiments and numerical simulations. Simulations use the lattice-Boltzmann method coupled with the discrete element method, providing detailed…

Soft Condensed Matter · Physics 2024-08-27 Zhuan Ge , Teng Man , Kimberly M. Hill , Yujie Wang , Sergio Andres Galindo-Torres

Gradient descent is commonly used to find minima in rough landscapes, particularly in recent machine learning applications. However, a theoretical understanding of why good solutions are found remains elusive, especially in strongly…

Machine Learning · Computer Science 2025-07-25 Tony Bonnaire , Giulio Biroli , Chiara Cammarota

Using statistical physics methods, we study generative diffusion models in the regime where the dimension of space and the number of data are large, and the score function has been trained optimally. Our analysis reveals three distinct…

Machine Learning · Computer Science 2025-01-08 Giulio Biroli , Tony Bonnaire , Valentin de Bortoli , Marc Mézard

The formation and dissolution of a droplet is an important mechanism related to various nucleation phenomena. Here, we address the droplet formation-dissolution transition in a two-dimensional Lennard-Jones gas to demonstrate a consistent…

Computational Physics · Physics 2018-12-19 Franz Paul Spitzner , Johannes Zierenberg , Wolfhard Janke

We experimentally study the vicinity of the Jamming transition by investigating the statics and the dynamics of the contact network of an horizontally shaken bi-disperse packing of photo-elastic discs. Compressing the packing very slowly,…

Soft Condensed Matter · Physics 2015-06-04 C. Coulais , R. P. Behringer , O. Dauchot

With respect to usual thermal ferromagnetic transitions, the zero-temperature finite-disorder critical point of the Random-field Ising model (RFIM) has the peculiarity to involve some 'droplet' exponent $\theta$ that enters the generalized…

Disordered Systems and Neural Networks · Physics 2011-07-21 Cecile Monthus , Thomas Garel

We develop a novel analytical dynamical analysis to derive precise energy density ratio evolutions for the $\phi$CDM and $poly\Lambda$CDM models, comparing them to the standard $\Lambda$CDM model and validating against numerical solutions.…

General Relativity and Quantum Cosmology · Physics 2026-03-25 Pierros Ntelis , Jackson Levi Said

In a recent publication we established an analogy between the free energy of a hard sphere system and the energy of an elastic network [1]. This result enables one to study the free energy landscape of hard spheres, in particular to define…

Disordered Systems and Neural Networks · Physics 2007-08-08 Carolina Brito , Matthieu Wyart

We study a system of particles which jump on the sites of the interval $[1,L]$ of $\mathbb Z$. The density at the boundaries is kept fixed to simulate the action of mass reservoirs. The evolution depends on two parameters $\lambda'\ge 0$…

Statistical Mechanics · Physics 2017-10-25 Matteo Colangeli , Anna De Masi , Errico Presutti

We consider the problem of approximating a function by an element of a nonlinear manifold which admits a differentiable parametrization, typical examples being neural networks with differentiable activation functions or tensor networks.…

Machine Learning · Computer Science 2026-04-20 Anthony Nouy , Agustín Somacal