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Statistical mechanics of infinite avalanches is studied in the framework of nonequilibrium random-field Ising model. Critical behavior of the model on a random graph (dilute Bethe lattice) is analyzed in detail. We show that sites with a…

Statistical Mechanics · Physics 2017-04-20 Prabodh Shukla , Diana Thongjaomayum

Quantum dynamics of the Bose-Hubbard Model is investigated through a semiclassical hamiltonian picture provided by the Time-Dependent Variational Principle method. The system is studied within a factorized slow/fast dynamics. The…

Superconductivity · Physics 2009-10-31 L. Amico , V. Penna

Symmetry concepts in parametrized dynamical systems may reduce the number of external parameters by a suitable normalization prescription. If, under the action of a symmetry group G, parameter space A becomes a (locally) trivial principal…

Populations and Evolution · Quantitative Biology 2023-05-30 Florian Nill

Multilevel or hierarchical data structures can occur in many areas of research, including economics, psychology, sociology, agriculture, medicine, and public health. Over the last 25 years, there has been increasing interest in developing…

Methodology · Statistics 2018-01-08 Bernet S. Kato , Carel F. W. Peeters

In this paper we consider the Higher Spin Six Vertex Model on the lattice $\mathbb{Z}_{\geq 2} \times \mathbb{Z}_{\geq 1}$. We first identify a family of translation invariant measures and subsequently we study the one point distribution of…

Mathematical Physics · Physics 2019-01-25 Takashi Imamura , Matteo Mucciconi , Tomohiro Sasamoto

In this work we obtain hierarchies of partial differential equations describing on-shell scalar products for two types of six-vertex models. More precisely, six-vertex models with two different diagonal boundary conditions are considered:…

Mathematical Physics · Physics 2015-10-06 W. Galleas , J. Lamers

Mathematical models are vital interpretive and predictive tools used to assist in the understanding of cell migration. There are typically two approaches to modelling cell migration: either micro-scale, discrete or macro-scale, continuum.…

Cell Behavior · Quantitative Biology 2018-08-16 Enrico Gavagnin , Christian A. Yates

A most important aspect in the field of traffic modeling is the simulation of bottleneck situations. For their realistic description a macroscopic multi-lane model for uni-directional freeways including acceleration, deceleration, velocity…

Statistical Mechanics · Physics 2009-10-31 Dirk Helbing

Statistical Shape Modeling (SSM) is a quantitative method for analyzing morphological variations in anatomical structures. These analyses often necessitate building models on targeted anatomical regions of interest to focus on specific…

Computer Vision and Pattern Recognition · Computer Science 2024-01-02 Hong Xu , Alan Morris , Shireen Y. Elhabian

A new framework for deriving equations of motion for constrained quantum systems is introduced, and a procedure for its implementation is outlined. In special cases the framework reduces to a quantum analogue of the Dirac theory of…

Quantum Physics · Physics 2013-09-13 Dorje C. Brody , Anna C. T. Gustavsson , Lane P. Hughston

The purpose of this paper is to explain the interest and importance of (approximate) models and model selection in Statistics. Starting from the very elementary example of histograms we present a general notion of finite dimensional model…

Statistics Theory · Mathematics 2007-06-13 Lucien Birgé

Many biological systems and artificial structures are ramified, and present a high geometric complexity. In this work, we propose a space-averaged model of branched systems for conservation laws. From a one-dimensional description of the…

Computational Physics · Physics 2014-10-13 Diego Lopez , Emmanuel de Langre , Sébastien Michelin

The two-dimensional Ising model is studied at the boundary of a half-infinite cylinder. The three regular lattices (square, triangular and hexagonal) and the three regimes (sub-, super- and critical) are discussed. The probability of having…

Statistical Mechanics · Physics 2009-09-23 Yvan Saint-Aubin , Louis-Pierre Arguin , Hassan Aurag

This paper addresses the well posedness of a dynamical model of perfect plasticity with mixed boundary conditions for general closed and convex elasticity sets. The proof relies on an asymptotic analysis of the solution of a perfect…

Analysis of PDEs · Mathematics 2022-02-16 Jean-François Babadjian , Randy Llerena

We give an abridged account of a continued string of studies in condensed matter physics and in complex systems that span five decades. We provide links to access abstracts and full texts of a selected list of publications. The studies were…

Statistical Mechanics · Physics 2024-01-15 Alberto Robledo , Carlos Velarde

Control-based continuation (CBC) is a general and systematic method to explore the dynamic response of a physical system and perform bifurcation analysis directly during experimental tests. Although CBC has been successfully demonstrated on…

Dynamical Systems · Mathematics 2024-11-05 Hamed Rezaee , Ludovic Renson

We study the inverse problem of deducing the dynamical characteristics (such as the potential field) of large systems from kinematic observations. We show that, for a class of steady-state systems, the solution is unique even with…

Astrophysics · Physics 2008-11-14 Mikko Kaasalainen

This work is devoted to the design of boundary controls of physical systems that are described by semilinear hyperbolic balance laws. A computational framework is presented that yields sufficient conditions for a boundary control to steer…

Optimization and Control · Mathematics 2022-08-15 Stephan Gerster , Felix Nagel , Aleksey Sikstel , Giuseppe Visconti

A variety of phenomena in nuclear and high energy physics seemingly do not satisfy the basic hypothesis for possible stationary states to be of the type covered by Boltzmann-Gibbs (BG) statistical mechanics. More specifically, the system…

Statistical Mechanics · Physics 2017-08-23 C. Tsallis , Ernesto P. Borges

Dependency networks (Heckerman et al., 2000) provide a flexible framework for modeling complex systems with many variables by combining independently learned local conditional distributions through pseudo-Gibbs sampling. Despite their…

Machine Learning · Computer Science 2026-04-02 Kazuya Takabatake , Shotaro Akaho
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