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Related papers: The Fermi-Ulam Accelerator Model Under Scaling Ana…

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The semiparametric accelerated failure time model is not as widely used as the Cox relative risk model mainly due to computational difficulties. Recent developments in least squares estimation and induced smoothing estimating equations…

Methodology · Statistics 2015-06-02 Steven Chiou , Junghi Kim , Jun Yan

Coupled dynamical systems with one slow element and many fast elements are analyzed. By averaging over the dynamics of the fast variables, the adiabatic kinetic branch is introduced for the dynamics of the slow variable in the adiabatic…

Chaotic Dynamics · Physics 2015-06-15 Hidetoshi Aoki , Kunihiko Kaneko

A compact method for amplitude calculations in theories with Dirac and Majorana effective operators is discussed. Using the renormalizable formalism of Denner et al., [1,2] for propagators, vertices and fermion (number) flow and introducing…

High Energy Physics - Phenomenology · Physics 2018-03-02 Michael Paraskevas

We briefly review some of the most relevant results that our group obtained in the past, while investigating the dynamics of the Fermi-Pasta-Ulam (FPU) models. A first result is the numerical evidence of the existence of two different kinds…

Statistical Mechanics · Physics 2016-08-31 Marco Pettini , Lapo Casetti , Monica Cerruti-Sola , Roberto Franzosi , E. G. D. Cohen

The Fokker-Planck Equation, applied to transport processes in fusion plasmas, can model several anomalous features, including uphill transport, scaling of confinement time with system size, and convective propagation of externally induced…

Plasma Physics · Physics 2007-11-05 D. F. Escande , F. Sattin

When a system is swept through a quantum critical point (QCP), the Kibble-Zurek mechanism predicts that the average number of topological defects follows a universal power-law scaling with the ramp time scale. This scaling behavior is…

Statistical Mechanics · Physics 2026-05-19 R. Jafari , Alireza Akbari

The probability density function of velocity fluctuations of {\em glanulence} observed by Radjai and Roux in their two-dimensional simulation of a slow granular flow under homogeneous quasistatic shearing is studied by the multifractal…

Soft Condensed Matter · Physics 2007-05-23 N. Arimitsu , T. Arimitsu

We investigate the universality of correlation functions of chaotic and disordered quantum systems as an external parameter is varied. A new, general scaling procedure is introduced which makes the theory invariant under reparametrizations.…

Condensed Matter · Physics 2010-03-09 P. Leboeuf , M. Sieber

Stress enhancement in the vicinity of brittle cracks makes the macro-scale failure properties extremely sensitive to the micro-scale material disorder. Therefore: (i) Fracturing systems often display a jerky dynamics, so-called crackling…

Statistical Mechanics · Physics 2010-01-04 D. Bonamy

By using the scaling method we derive the virial theorem for the relativistic mean field model of nuclei treated in the Thomas-Fermi approach. The Thomas-Fermi solutions statisfy the stability condition against scaling. We apply the…

Nuclear Theory · Physics 2010-12-23 S. K. Patra , M. Centelles , X. Vinas , M. Del Estal

The scaling properties encompass in a simple analysis many of the volatility characteristics of financial markets. That is why we use them to probe the different degree of markets development. We empirically study the scaling properties of…

Statistical Mechanics · Physics 2008-12-02 T. Di Matteo , T. Aste , M. M. Dacorogna

Random features (RFs) are a popular technique to scale up kernel methods in machine learning, replacing exact kernel evaluations with stochastic Monte Carlo estimates. They underpin models as diverse as efficient transformers (by…

Machine Learning · Statistics 2024-10-04 Isaac Reid , Stratis Markou , Krzysztof Choromanski , Richard E. Turner , Adrian Weller

A discrete binomial random-walk description of molecular collisions is used to quantify the variance of coarse-grained velocity fields arising solely from collision-induced momentum exchange. Closed-form expressions for the growth of…

Chemical Physics · Physics 2026-02-26 Tristan Barkman

Standard deep learning methods, such as Ensemble Models, Bayesian Neural Networks and Quantile Regression Models provide estimates to prediction uncertainties for data-driven deep learning models. However, they can be limited in their…

This paper considers both the least squares and quasi-maximum likelihood estimation for the recently proposed scalable ARMA model, a parametric infinite-order vector AR model, and their asymptotic normality is also established. It makes…

Methodology · Statistics 2024-06-28 Yuchang Lin , Wenyu Li , Qianqian Zhu , Guodong Li

Many applications require that we learn the parameters of a model from data. EM is a method used to learn the parameters of probabilistic models for which the data for some of the variables in the models is either missing or hidden. There…

Machine Learning · Computer Science 2013-01-30 Luis E. Ortiz , Leslie Pack Kaelbling

Reliability analysis is a sub-field of uncertainty quantification that assesses the probability of a system performing as intended under various uncertainties. Traditionally, this analysis relies on deterministic models, where experiments…

Computation · Statistics 2026-05-19 Anderson V. Pires , Maliki Moustapha , Stefano Marelli , Bruno Sudret

The pioneering computer simulations of the energy relaxation mechanisms performed by Fermi, Pasta and Ulam can be considered as the first attempt of understanding energy relaxation and thus heat conduction in lattices of nonlinear…

Statistical Mechanics · Physics 2009-11-10 Stefano Lepri , Roberto Livi , Antonio Politi

We build a minimal, mean-field, model of plasticity of amorphous solids, based upon a phenomenology of dissipative events derived, in a preceding paper [A. Lemaitre, C. Caroli, arXiv:0705.0823] from extensive molecular simulations. It…

Disordered Systems and Neural Networks · Physics 2016-08-14 Anaël Lemaître , Christiane Caroli

Mechanical deformation of amorphous solids can be described as consisting of an elastic part in which the stress increases linearly with strain, up to a yield point at which the solid either fractures or starts deforming plastically. It is…

Disordered Systems and Neural Networks · Physics 2020-03-25 Baoshuang Shang , Pengfei Guan , Jean-Louis Barrat
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