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Coarse-graining is central to reducing dimensionality in molecular dynamics, and is typically characterized by a mapping which projects the full state of the system to a smaller class of variables. While extensive literature has been…

Probability · Mathematics 2020-01-08 Frédéric Legoll , Tony Lelièvre , Upanshu Sharma

Thermodynamic irreversibility is a fundamental concept in statistical physics, yet its experimental measurement remains challenging, especially for complex systems. We introduce a novel random coarse-graining framework to identify…

Statistical Mechanics · Physics 2025-09-25 Ruicheng Bao , Naruo Ohga , Sosuke Ito

Optically confined colloidal particles, when placed in close proximity, form a dissipatively coupled system through hydrodynamic interactions. The role of such interactions influencing irreversibility and energy dissipation in…

We review some recent coarse-graining and multi-scale methods, but also put forward some new ideas for addressing such issues. We find that, if one is guided by nonequilibrium statistical mechanics and thermodynamics, it is possible to…

Soft Condensed Matter · Physics 2009-11-06 Patrick Ilg , Vlasis Mavrantzas , Hans Christian Öttinger

Many biological systems can be described by finite Markov models. A general method for simplifying master equations is presented that is based on merging adjacent states. The approach preserves the steady-state probability distribution and…

Biological Physics · Physics 2021-03-01 David Seiferth , Peter Sollich , Stefan Klumpp

Multiscale systems are ubiquitous in science and technology, but are notoriously challenging to simulate as short spatiotemporal scales must be appropriately linked to emergent bulk physics. When expensive high-dimensional dynamical systems…

Machine Learning · Computer Science 2025-12-30 Quercus Hernandez , Max Win , Thomas C. O'Connor , Paulo E. Arratia , Nathaniel Trask

Recently a number of approaches has been developed to connect the microscopic dynamics of particle systems to the macroscopic properties of systems in nonequilibrium stationary states, via the theory of dynamical systems. This way a direct…

Statistical Mechanics · Physics 2009-10-31 L. Rondoni , E. G. D. Cohen

Coarse-graining or model reduction is a term describing a range of approaches used to extend the time-scale of molecular simulations by reducing the number of degrees of freedom. In the context of molecular simulation, standard…

Dynamical Systems · Mathematics 2023-11-14 Thomas Hudson , Xingjie Helen Li

Coarse-graining techniques play a central role in reducing the complexity of stochastic models, and are typically characterised by a mapping which projects the full state of the system onto a smaller set of variables which captures the…

Probability · Mathematics 2023-09-28 Bastian Hilder , Upanshu Sharma

Coarse-graining has become an area of tremendous importance within many different research fields. For molecular simulation, coarse-graining bears the promise of finding simplified models such that long-time simulations of large-scale…

Chemical Physics · Physics 2019-09-04 Feliks Nüske , Lorenzo Boninsegna , Cecilia Clementi

In many systems we can describe emergent macroscopic behaviors, quantitatively, using models that are much simpler than the underlying microscopic interactions; we understand the success of this simplification through the renormalization…

Biological Physics · Physics 2019-01-01 Leenoy Meshulam , Jeffrey L. Gauthier , Carlos D. Brody , David W. Tank , William Bialek

Simulations of condensed matter systems often focus on the dynamics of a few distinguished components but require integrating the dynamics of the full system. A prime example is a molecular dynamics simulation of a (macro)molecule in…

Computational Physics · Physics 2024-03-12 Mauricio J. del Razo , Daan Crommelin , Peter G. Bolhuis

Starting from a coarse-grained map of a quantum many-body system, we construct the inverse map that assigns a microscopic state to a coarse-grained state based on the maximum entropy principle. Assuming unitary evolution in the microscopic…

Quantum Physics · Physics 2025-09-05 Adán Castillo , Carlos Pineda , Erick Sebastían Navarrete , David Davalos

In active matter, the lack of momentum conservation makes non-reciprocal interactions the rule rather than the exception. They lead to a rich set of emerging behaviors that are hard to account for and to predict starting from the…

Statistical Mechanics · Physics 2024-02-06 Alberto Dinelli , Jérémy O'Byrne , Agnese Curatolo , Yongfeng Zhao , Peter Sollich , Julien Tailleur

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

A model reduction technique based on an optimization principle is employed to coarse-grain inviscid, incompressible fluid dynamics in two dimensions. In this reduction the spectrally-truncated vorticity equation defines the microdynamics,…

Fluid Dynamics · Physics 2016-09-21 Bruce Turkington , Qian-Yong Chen , Simon Thalabard

A generalized understanding of protein dynamics is an unsolved scientific problem, the solution of which is critical to the interpretation of the structure-function relationships that govern essential biological processes. Here, we approach…

This work presents a rigorous framework based on coarse-graining to analyze highly compressible turbulence. We show how the requirement that viscous effects on the dynamics of large-scale momentum and kinetic energy be negligible ---an…

Fluid Dynamics · Physics 2012-12-27 Hussein Aluie

Active matter, responsive ("smart") materials and materials under time-dependent load are systems out of thermal equilibrium. To construct coarse-grained models for such systems, one needs to integrate out a distribution of microstates that…

Statistical Mechanics · Physics 2022-05-17 Tanja Schilling

Multiscale molecular modeling is widely applied in scientific research of molecular properties over large time and length scales. Two specific challenges are commonly present in multiscale modeling, provided that information between the…

Computational Physics · Physics 2024-07-23 Jun Zhang , Xiaohan Lin , Weinan E , Yi Qin Gao
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