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The influence of an external flow on the relaxation dynamics of a single polymer is investigated theoretically and numerically. We show that a pronounced dynamical slowdown occurs in the vicinity of the coil-stretch transition, especially…

Chaotic Dynamics · Physics 2007-05-23 Antonio Celani , Alberto Puliafito , Dario Vincenzi

This paper proposes a novel dynamic forecasting method using a new supervised Principal Component Analysis (PCA) when a large number of predictors are available. The new supervised PCA provides an effective way to bridge the gap between…

Econometrics · Economics 2024-06-14 Zhaoxing Gao , Ruey S. Tsay

Dipolar fluids are known to exhibit complex self-assembly at low temperatures, yet a compact thermodynamic description of their aggregate statistics has remained elusive. Using molecular dynamics simulations of Stockmayer particles with a…

Chemical Physics · Physics 2026-04-23 Zhongqi Liang , Jesús Peréz-Ríos

A recently introduced particle-based model for fluid dynamics with effective excluded volume interactions is analyzed in detail. The interactions are modeled by means of stochastic multiparticle collisions which are biased and depend on…

Soft Condensed Matter · Physics 2007-05-23 Thomas Ihle , Erkan Tuzel

We introduce a framework for model reduction of chain models for dissipative particle dynamics (DPD) simulations, where the characteristic size of the chain, pressure, density, and temperature are preserved. The proposed methodology reduces…

Soft Condensed Matter · Physics 2016-05-04 Nicolas Moreno , Suzana P. Nunes , Victor M. Calo

Recently it has been shown that a two-dimensional model of self-attracting polymers based on attracting segments displays two phase transitions, a theta-like collapse between swollen polymers and a globular state and another between the…

Soft Condensed Matter · Physics 2008-07-10 J. Krawczyk , A. L. Owczarek , T. Prellberg

Dynamic density functionals (DDFs) are popular tools for studying the dynamical evolution of inhomogeneous polymer systems. Here, we present a systematic evaluation of a set of diffusive DDF theories by comparing their predictions with data…

Soft Condensed Matter · Physics 2018-02-07 Shuanhu Qi , Friederike Schmid

We derive a thermodynamically consistent framework for incorporating entanglement dynamics into constitutive equations for flowing polymer melts. We use this to combine the convected constraint release (CCR) dynamics of…

Soft Condensed Matter · Physics 2022-12-21 Benjamin E. Dolata , Peter D. Olmsted

In this review paper, we will present different data-driven dimension reduction techniques for dynamical systems that are based on transfer operator theory as well as methods to approximate transfer operators and their eigenvalues,…

Dynamical Systems · Mathematics 2019-02-26 Stefan Klus , Feliks Nüske , Péter Koltai , Hao Wu , Ioannis Kevrekidis , Christof Schütte , Frank Noé

The coupled dynamics of entangled polymers which span a broad time and length scales govern the unique viscoelastic properties of polymers. To follow chain mobility by numerical simulations from the intermediate Rouse and reptation regimes…

Soft Condensed Matter · Physics 2016-11-03 Gary S. Grest

In this work, we develop a novel principal component analysis (PCA) for semimartingales by introducing a suitable spectral analysis for the quadratic variation operator. Motivated by high-dimensional complex systems typically found in…

Statistics Theory · Mathematics 2016-03-10 Alberto Ohashi , Alexandre B Simas

Dynamic inner principal component analysis (DiPCA) is a powerful method for the analysis of time-dependent multivariate data. DiPCA extracts dynamic latent variables that capture the most dominant temporal trends by solving a large-scale,…

Systems and Control · Electrical Eng. & Systems 2020-03-16 Sungho Shin , Alex D. Smith , S. Joe Qin , Victor M. Zavala

Sequential or online dimensional reduction is of interests due to the explosion of streaming data based applications and the requirement of adaptive statistical modeling, in many emerging fields, such as the modeling of energy end-use…

Machine Learning · Statistics 2014-07-17 Zhaoyi Kang , Costas J. Spanos

We perform numerical simulations of isolated, partially active polymers, driven out-of-equilibrium by a fraction of their monomers. We show that, if the active beads are all gathered in a contiguous block, the position of the section along…

Soft Condensed Matter · Physics 2023-09-04 Marin Vatin , Sumanta Kundu , Emanuele Locatelli

Principal component analysis (PCA) is widely used for dimensionality reduction, with well-documented merits in various applications involving high-dimensional data, including computer vision, preference measurement, and bioinformatics. In…

Machine Learning · Statistics 2013-10-01 Gonzalo Mateos , Georgios B. Giannakis

Hybrid molecular dynamics/Monte Carlo simulations used to study melts of unentangled, thermoreversibly associating supramolecular polymers. In this first of a series of papers, we describe and validate a model that is effective in…

Soft Condensed Matter · Physics 2015-05-13 Robert S. Hoy , Glenn H. Fredrickson

Principal component analysis (PCA) is a classical method for dimensionality reduction based on extracting the dominant eigenvectors of the sample covariance matrix. However, PCA is well known to behave poorly in the ``large $p$, small $n$''…

Statistics Theory · Mathematics 2009-08-26 Arash A. Amini , Martin J. Wainwright

The linear viscoelastic response of flexible polymer solutions in the dilute and semidilute unentangled regimes is investigated using Brownian dynamics simulations. The relaxation modulus and dynamic moduli are computed over a wide range of…

Soft Condensed Matter · Physics 2026-04-08 Amit Varakhedkar , P. Sunthar , J. Ravi Prakash

This paper studies the numerical deformation that time-domain integration (TDI) methods introduce to the shape of the coupling between the dynamic modes and variables of power system models. To this aim, we employ a small-signal stability…

Numerical Analysis · Mathematics 2023-04-11 Carlo Tajoli , Georgios Tzounas , Gabriela Hug

The principal component analysis (PCA), a mathematical tool commonly used in statistics, has recently been employed to interpret the $p_T$-dependent fluctuations of harmonic flow $v_n$ in terms of leading and subleading flow modes in heavy…

Nuclear Experiment · Physics 2020-08-26 Ziming Liu , Arabinda Behera , Huichao Song , Jiangyong Jia