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We develop a coarse grained (CG) approach for efficiently simulating calcium dynamics in the endoplasmic reticulum membrane based on a fine stochastic lattice gas model. By grouping neighboring microscopic sites together into CG cells and…

Chemical Physics · Physics 2015-06-15 Chuansheng Shen , Hanshuang Chen

In the present article, novel Coarse-Graining (CG) algorithms for the Eulerian-Lagrangian (EL) simulation of particle-laden flows are proposed. These include different variants of Reproducing Kernel Particle Methods (RKPM) and an extended…

Fluid Dynamics · Physics 2025-11-12 H. Eshraghi , E. Amani , M. Saffar-Avval

Coarse graining is an important ingredient in many multi-scale continuum-discrete solvers such as CFD--DEM (computational fluid dynamics--discrete element method) solvers for dense particle-laden flows. Although CFD--DEM solvers have become…

Computational Physics · Physics 2015-08-14 Rui Sun , Heng Xiao

We derive a coarse-grained description of the dynamics of a nanoparticle immersed in an isothermal simple fluid by performing a systematic coarse graining of the underlying microscopic dynamics. As coarse-grained or relevant variables we…

Mesoscale and Nanoscale Physics · Physics 2016-01-20 Pep Español , Aleksandar Donev

A novel principle is presented which allows for the proof of bounded weak solutions to a class of physically relevant, strongly coupled parabolic systems exhibiting a formal gradient-flow structure. The main feature of these systems is that…

Analysis of PDEs · Mathematics 2015-06-11 Ansgar Jüngel

In this work, a coarse-graining method previously proposed by the authors in a companion paper based on solving diffusion equations is applied to CFD-DEM simulations, where coarse graining is used to obtain solid volume fraction, particle…

Computational Physics · Physics 2015-01-07 Rui Sun , Heng Xiao

Coarse-grained (CG) molecular dynamics enables the study of biological processes at temporal and spatial scales that would be intractable at an atomistic resolution. However, accurately learning a CG force field remains a challenge. In this…

Coarse-grained models are a core computational tool in theoretical chemistry and biophysics. A judicious choice of a coarse-grained model can yield physical insight by isolating the essential degrees of freedom that dictate the…

Statistical Mechanics · Physics 2023-04-12 Shriram Chennakesavalu , David J. Toomer , Grant M. Rotskoff

Conjugated organic molecules play a central role in a wide range of optoelectronic devices, including organic light-emitting diodes, organic field-effect transistors, and organic solar cells. A major bottleneck in the computational design…

We propose a data-driven, coarse-graining formulation in the context of equilibrium statistical mechanics. In contrast to existing techniques which are based on a fine-to-coarse map, we adopt the opposite strategy by prescribing a…

Machine Learning · Statistics 2017-02-01 Markus Schöberl , Nicholas Zabaras , Phaedon-Stelios Koutsourelakis

Although an intimate relation between entropy and diffusion has been advocated for many years and even seems to have been verified in theory and experiments, a quantitatively reliable study, and any derivation of an algebraic relation…

Statistical Mechanics · Physics 2020-07-22 Subhajit Acharya , Biman Bagchi

Data-based discovery of effective, coarse-grained (CG) models of high-dimensional dynamical systems presents a unique challenge in computational physics and particularly in the context of multiscale problems. The present paper offers a…

Computational Physics · Physics 2020-08-26 Sebastian Kaltenbach , Phaedon-Stelios Koutsourelakis

We investigate the response of a system of hard spheres to two classes of perturbations over a range of densities spanning the fluid, crystalline, and glassy regimes within a molecular dynamics framework. Firstly, we consider the relaxation…

Statistical Mechanics · Physics 2025-09-04 Matthew Kafker , Xerxes D. Arsiwalla

We present a systematic coarse-graining (CG) strategy for many particle molecular systems based on cluster expansion techniques. We construct a hierarchy of coarse-grained Hamiltonians with interaction potentials consisting of two, three…

Soft Condensed Matter · Physics 2017-10-11 Anastasios Tsourtis , Vagelis Harmandaris , Dimitrios Tsagkarogiannis

While accurate simulations of dense gas flows far from the equilibrium can be achieved by Direct Simulation adapted to the Enskog equation, the significant computational demand required for collisions appears as a major constraint. In order…

Computational Physics · Physics 2023-08-11 Mohsen Sadr , M. Hossein Gorji

This work presents a novel framework governing the development of an efficient, accurate, and transferable coarse-grained (CG) model of a polyether material. The proposed framework combines the two fundamentally different classical…

We investigate shear-induced crystallization in a very dense flow of mono-disperse inelastic hard spheres. We consider a steady plane Couette flow under constant pressure and neglect gravity. We assume that the granular density is greater…

Soft Condensed Matter · Physics 2009-11-11 Evgeniy Khain , Baruch Meerson

We develop coarse-grained particle approaches for studying the elastic mechanics of vesicles with heterogeneous membranes having phase-separated domains. We perform simulations both of passive shape fluctuations and of active systems where…

Soft Condensed Matter · Physics 2023-02-28 David A. Rower , Paul J. Atzberger

An impurity particle coupling to its host fluid via inelastic hard sphere collisions is considered. It is shown that the exact equation for its distribution function can be mapped onto that for an impurity with elastic collisions and an…

Statistical Mechanics · Physics 2007-05-23 A. Santos , J. W. Dufty

Diffusion-a measure of dynamics, and entropy-a measure of disorder in the system, are found to be intimately correlated in many systems, and the correlation is often strongly non-linear. We explore the origin of this complex dependence by…

Soft Condensed Matter · Physics 2015-12-09 Kazuhiko Seki , Biman Bagchi