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In this chapter we review the rich behavior of polymer chains embedded in a quenched random environment. We first consider the problem of a Gaussian chain free to move in a random potential with short-ranged correlations. We derive the…

Disordered Systems and Neural Networks · Physics 2007-05-23 Yadin Y. Goldschmidt , Yohannes Shiferaw

In this paper we consider in detail the connection between the problem of a polymer in a random medium and that of a quantum particle in a random potential. We are interested in a system of finite volume where the polymer is known to be…

Disordered Systems and Neural Networks · Physics 2009-10-31 Yohannes Shiferaw , Yadin Y. Goldschmidt

We present an exactly solvable model of a Gaussian (flexible) polymer chain in a quenched random medium. This is the case when the random medium obeys very long range quadratic correlations. The model is solved in $d$ spatial dimensions…

Disordered Systems and Neural Networks · Physics 2009-10-31 Yohannes Shiferaw , Yadin Y. Goldschmidt

Motivated by the observation of the storage of excess elastic free energy - (prestress) -- in cross linked semiflexible networks, we consider the problem of the conformational statistics of a single semiflexible polymer in a quenched random…

Soft Condensed Matter · Physics 2019-04-10 Valentin M. Slepukhin , Maximilian J. Grill , Kei W. Müller , Wolfgang A. Wall , Alex J. Levine

In this paper, we employ Molecular Dynamics computer simulations to study and compare the statics and dynamics of linear and circular (ring) polymer chains in entangled solutions of different densities. While we confirm that linear chain…

Soft Condensed Matter · Physics 2016-02-17 Negar Nahali , Angelo Rosa

In this review we provide an organized summary of the theoretical and computational results which are available for polymers subject to spatial or topological constraints. Because of the interdisciplinary character of the topic, we provide…

Statistical Mechanics · Physics 2015-03-19 Cristian Micheletti , Davide Marenduzzo , Enzo Orlandini

We study the regularity of densities of distributions that are polynomial images of the standard Gaussian measure on $\mathbb{R}^n$. We assume that the degree of a polynomial is fixed and that each variable enters to a power bounded by…

Probability · Mathematics 2020-07-28 Egor Kosov

We have studied a model of a random walk in a quenched random environment. In addition to featuring anomalous diffusion and localization, for special regimes of disorder parameters the particle density decomposes into multi-Gaussian…

Statistical Mechanics · Physics 2010-11-24 Tapio Simula , Mikko Stenlund

We present using simple scaling arguments and one step replica symmetry breaking a theory for the localization of semiflexible polymers in a quenched random environment. In contrast to completely flexible polymers, localization of…

Soft Condensed Matter · Physics 2009-11-10 Arti Dua , Thomas A. Vilgis

We study self avoiding random walks in an environment where sites are excluded randomly, in two and three dimensions. For a single polymer chain, we study the statistics of the time averaged monomer density and show that these are well…

Soft Condensed Matter · Physics 2015-05-19 J. M. Deutsch , M. Olvera de la Cruz

Based on a recently established formalism (U. Ebert, J. Stat. Phys. 82, 183 (1996)) we analyze the diffusive motion of a long polymer in a quenched random medium. The medium is modeled by a frozen semidilute polymer system. In the framework…

Statistical Mechanics · Physics 2015-06-25 Stefan Mueller

This paper focuses on the time-harmonic electromagnetic (EM) scattering problem in a general medium which may possess a nontrivial topological structure. We model this by an inhomogeneous and possibly anisotropic medium with embedded…

Analysis of PDEs · Mathematics 2024-08-14 Huaian Diao , Hongyu Liu , Qingle Meng , Li Wang

The equivalent permeability of a randomly cracked porous material is studied using a finite element program in which a four-nodes zero-thickness element is implemented for modelling the cracks. The numerical simulations are performed for…

Classical Physics · Physics 2008-10-07 Siavash Ghabezloo , Ahmad Pouya

Mixtures of Gaussian (or normal) distributions arise in a variety of application areas. Many heuristics have been proposed for the task of finding the component Gaussians given samples from the mixture, such as the EM algorithm, a…

Probability · Mathematics 2007-05-23 Sanjeev Arora , Ravi Kannan

In this paper we investigate the problem of a long self-avoiding polymer chain immersed in a random medium. We find that in the limit of a very long chain and when the self-avoiding interaction is weak, the conformation of the chain…

Disordered Systems and Neural Networks · Physics 2009-11-07 Yadin Y. Goldschmidt , Yohannes Shiferaw

The tube model is a central concept in polymer physics, and allows to reduce the complex many-filament problem of an entangled polymer solution to a single filament description. We investigate the probability distribution function of…

Soft Condensed Matter · Physics 2009-08-12 Hauke Hinsch , Erwin Frey

We develop a strong stretching approximation for a polymer brush made of self-avoiding polymer chains. The density profile of the brush and the distribution of the end monomer positions in stretching direction are computed and compared with…

Soft Condensed Matter · Physics 2021-05-26 Dirk Romeis , Michael Lang

In this paper we revisit the problem of a (non self-avoiding) polymer chain in a random medium which was previously investigated by Edwards and Muthukumar (EM). As noticed by Cates and Ball (CB) there is a discrepancy between the…

Disordered Systems and Neural Networks · Physics 2009-10-31 Yadin Y. Goldschmidt

The statistical mechanics of polymer loops entangled in the two-dimensional array of randomly distributed obstacles of infinite length is discussed. The area of the loop projected to the plane perpendicular to the obstacles is used as a…

Condensed Matter · Physics 2009-10-28 Matthias Otto , Thomas A. Vilgis

Introducing inequality constraints in Gaussian process (GP) models can lead to more realistic uncertainties in learning a great variety of real-world problems. We consider the finite-dimensional Gaussian approach from Maatouk and Bay (2017)…

Machine Learning · Statistics 2021-11-04 Andrés F. López-Lopera , François Bachoc , Nicolas Durrande , Olivier Roustant
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