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We report on the linear viscoelastic properties of mixtures comprising multiarm star (as model soft colloids) and long linear chain homopolymers in a good solvent. In contrast to earlier works, we investigated symmetric mixtures (with a…

Soft Condensed Matter · Physics 2020-07-01 Daniele Parisi , Domenico Truzzolillo , Vishnu D. Deepak , Mario Gauthier , Dimitris Vlassopoulos

Molecular dynamics simulation methods are used to study the folding of polymer chains into packed cubic states. The polymer model, based on a chain of linked sites moving in the continuum, includes both excluded volume and torsional…

Soft Condensed Matter · Physics 2009-11-10 D. C. Rapaport

We calculate statistical properties of amorphous polymer chains between crystalline lamellae by self-consistent field model simulations. In our model, an amorphous subchain is modelled as a polymer chain of which ends are grafted onto the…

Soft Condensed Matter · Physics 2015-06-23 Takashi Uneyama , Takafumi Miyata , Koh-hei Nitta

Complex dynamical systems which are governed by anomalous diffusion often can be described by Langevin equations driven by L\'evy stable noise. In this article we generalize nonlinear stochastic differential equations driven by Gaussian…

Statistical Mechanics · Physics 2015-06-18 Rytis Kazakevicius , Julius Ruseckas

Adsorption is a relevant process in many fields, such as product manufacturing or pollution remediation in porous materials. Adsorption takes place at the molecular scale, amenable to be modeled by Lagrangian numerical methods. We have…

Machine Learning · Statistics 2020-04-15 Maryam Rahbaralam , Amir Abdollahi , Daniel Fernàndez-Garcia , Xavier Sanchez-Vila

At high levels, the asymptotic distribution of a stationary, regularly varying Markov chain is conveniently given by its tail process. The latter takes the form of a geometric random walk, the increment distribution depending on the sign of…

Methodology · Statistics 2014-12-11 Holger Drees , Johan Segers , Michał Warchoł

Nonparametric Bayesian approaches based on Gaussian processes have recently become popular in the empirical learning community. They encompass many classical methods of statistics, like Radial Basis Functions or various splines, and are…

Data Analysis, Statistics and Probability · Physics 2007-05-23 J. C. Lemm

Taking into account the nonequivalence of fixed-force and fixed-length ensembles in the weak-force regime, equations of state are derived that describe the equilibrium extension or compression of an ideal Gaussian polymer chain in response…

Soft Condensed Matter · Physics 2007-05-23 Richard M. Neumann

A Bayesian non-parametric framework for studying time-to-event data is proposed, where the prior distribution is allowed to depend on an additional random source, and may update with the sample size. Such scenarios are natural, for…

Methodology · Statistics 2025-05-06 Martin Bladt , Jorge González Cázares

The field theory approach to the statistical mechanics of a system of N polymer rings linked together is generalized to the case of links that have a fixed number $2s$ of maxima and minima. Such kind of links are called plats and appear for…

Statistical Mechanics · Physics 2018-07-11 Franco Ferrari , Jarosław Paturej , Marcin Piątek , Yani Zhao

The role of solvent quality in determining the universal material properties of dilute polymer solutions undergoing steady simple shear flow is examined. A bead-spring chain representation of the polymer molecule is used, and the influence…

Soft Condensed Matter · Physics 2020-07-03 K. Satheesh Kumar , J. Ravi Prakash

Gaussian processes (GPs) are commonplace in spatial statistics. Although many non-stationary models have been developed, there is arguably a lack of flexibility compared to equipping each location with its own parameters. However, the…

Machine Learning · Statistics 2018-07-19 Leo L. Duan , Xia Wang , Rhonda D. Szczesniak

A generalization of the ABC model, a one-dimensional model of a driven system of three particle species with local dynamics, is introduced, in which the model evolves under either (i) density-conserving or (ii) nonconserving dynamics. For…

Statistical Mechanics · Physics 2015-05-19 A. Lederhendler , D. Mukamel

Conformational properties of regular dendrimers and more general hyperbranched polymer stars with Gaussian statistics for the spacer chains between branching points are revisited numerically. We investigate the scaling for asymptotically…

Soft Condensed Matter · Physics 2015-08-18 P. Polinska , C. Gillig , J. P. Wittmer , J. Baschnagel

Efficient assessment of convolved hidden Markov models is discussed. The bottom-layer is defined as an unobservable categorical first-order Markov chain, while the middle-layer is assumed to be a Gaussian spatial variable conditional on the…

Geophysics · Physics 2017-10-19 Torstein Fjeldstad , Henning Omre

Polymer chains dissolved in a solvent take random conformations due to large internal degrees of freedom and are characterized geometrically by their average shape and size. The diffusive dynamics of such large macromolecules play an…

Soft Condensed Matter · Physics 2023-07-11 Prabeen Kumar Pattnayak , Aloke Kumar , Gaurav Tomar

In the environmental modeling field, the exploratory analysis of responses often exhibits spatial correlation as well as some non-Gaussian attributes such as skewness and/or heavy-tailedness. Consequently, we propose a general spatial model…

Statistics Theory · Mathematics 2019-07-25 Behzad Mahmoudian

Using the LePage representation, a strictly stable random element in a Banach space with $\alpha\in(0,2)$ can be represented as a sum of points of a Poisson process. This point process is union-stable, i.e. the union of its two independent…

Probability · Mathematics 2007-05-23 Youri Davydov , Ilya Molchanov , Sergei Zuyev

A soft ellipsoid model for Gaussian polymer chains is studied, following an idea proposed by Murat and Kremer [J. Chem. Phys. 108, 4340 (1998)]. In this model chain molecules are mapped onto ellipsoids with certain shapes, and to each shape…

Soft Condensed Matter · Physics 2009-10-31 Frank Eurich , Philipp Maass

In this work, we give a ${\rm poly}(d,k)$ time and sample algorithm for efficiently learning the parameters of a mixture of $k$ spherical distributions in $d$ dimensions. Unlike all previous methods, our techniques apply to heavy-tailed…

Data Structures and Algorithms · Computer Science 2026-05-22 Alkis Kalavasis , Pravesh K. Kothari , Shuchen Li , Manolis Zampetakis