Article
Computer Science, Interdisciplinary Applications
Joseph Bakarji, Daniel M. Tartakovsky
Summary: Statistical (machine learning) tools for equation discovery require large amounts of data, typically computer generated rather than experimentally observed. Learning on simulated data in areas such as multiscale modeling and stochastic simulations can lead to discovery. Our machine-learning strategy based on sparse regression replaces human discovery of models and can be executed in two modes.
JOURNAL OF COMPUTATIONAL PHYSICS
(2021)
Article
Chemistry, Physical
Shu Wang, Zhan Ma, Wenxiao Pan
Summary: This work focuses on building accurate and efficient coarse-grained models for non-equilibrium systems by using dynamic equations and non-stationary generalized Langevin equations, preserving non-equilibrium dynamics and being applicable to any non-equilibrium process and observable of interest.
Article
Mechanics
Pavel Castro-Villarreal, Claudio Contreras-Aburto, Sendic Estrada-Jimenez, Idrish Huet-Hernandez, Oscar Vazquez-Rodriguez
Summary: The study examines the stochastic dynamics of a tagged Brownian particle in an interacting system using the path-integral representation and contrasts it with the standard equations. It investigates the single-file diffusion phenomenon and the mean-square displacement of the tracer particle in terms of Bessel functions. Additionally, it explores the behavior of a Brownian particle system with paramagnetic interactions near crystallization using a perturbation treatment and validates the findings through Brownian dynamics simulation.
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT
(2021)
Article
Physics, Fluids & Plasmas
I. G. Marchenko, V. Aksenova, I. I. Marchenko, J. Luczka, J. Spiechowicz
Summary: We investigate the impact of temperature on the diffusion coefficient of an inertial Brownian particle in a symmetric periodic potential under the influence of a symmetric time-periodic force. It is observed that the diffusion coefficient exhibits giant damped quasiperiodic oscillations in the low-friction regime. Our research demonstrates that the diffusion coefficient increases at its minima when temperature rises, while it decreases at the maxima within a finite temperature range. This phenomenon can be explained by considering the perturbation of deterministic dynamics by thermal fluctuations and the mean residence time of the particle in locked and running trajectories. Moreover, we show that the temperature dependence of the diffusion coefficient can be accurately reconstructed using the stationary probability distribution of the running trajectories.
Article
Mechanics
Dario Lucente, Andrea Puglisi, Massimiliano Viale, Angelo Vulpiani
Summary: We study the non-equilibrium properties of a class of Markov processes driven by a mixture of Gaussian and Poissonian noises. We show that detailed balance does not hold even when correlation functions are symmetric under time reversal. Higher order correlation functions can reveal a breakdown of time reversal symmetry. We provide analytical expressions for the average entropy production rate and introduce a scale dependent estimate for entropy production. We also revisit the Brownian gyrator and show that its entropy production is positive but minimal when Onsager relations are satisfied. We discuss estimates of entropy production for partially accessible systems and compare our results with thermodynamic uncertainty relations.
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT
(2023)
Article
Mechanics
E. Gravanis, E. Akylas, G. Livadiotis
Summary: The diffusion of particles with kappa distributed velocities is strongly affected by statistical correlations. The study shows that the superstatistics interpretation is fundamental for understanding kappa distribution, while focusing on a single degree of freedom is inconsistent with this interpretation. The authors also find that the mean energy per degree of freedom is the superstatistical fluctuating temperature in a system with a large number of particles, highlighting the importance of considering correlated degrees of freedom.
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT
(2021)
Article
Mechanics
A. Imparato
Summary: A 1D model of interacting particles moving on a periodic substrate and in a position dependent temperature profile shows that when the substrate and temperature profile are spatially asymmetric, a centre-of-mass velocity develops which leads to a directed transport of the chain. By tuning the model parameters, particles can transition from an ordered to disordered configuration, with the maximal motor effect achieved in the disordered phase. Collective effects play a crucial role in enhancing the dynamic and thermodynamic performances of microscopic machines.
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT
(2021)
Article
Biochemistry & Molecular Biology
Adam Liwo, Cezary Czaplewski, Adam K. Sieradzan, Agnieszka G. Lipska, Sergey A. Samsonov, Rajesh K. Murarka
Summary: This review article discusses the physical basis, force fields, equations of motion, numerical integration algorithms, and applications of coarse-grained molecular dynamics. By integrating out secondary degrees of freedom, the motion of coarse-grained sites is controlled, leading to simulations at a coarse-grained level.
Article
Mechanics
Pedro Paraguassu, Welles A. M. Morgado
Summary: This paper analytically derives the expression for the distribution of heat in a diffusive system in a logarithm potential through path integral. The found distribution is applied to the first passage problem, resulting in unexpected results for the reversibility of the distribution and a fluctuation theorem under specific conditions of the strength parameters.
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT
(2021)
Article
Mechanics
Carlos Lajusticia-Costan, Silvia N. Santalla, Javier Rodriguez-Laguna, Elka Korutcheva
Summary: This paper discusses random walkers that deform the medium as they move, creating an effective attraction between walkers mediated by the medium, which can be regarded as a space metric. In the strong-deformability regime, diffusion is initially described by the porous medium equation, leading to subdiffusive behavior of an initially localized cloud of particles. The differences in growth rates can be explained by strong correlations between particles, explored through fluctuations of the center of mass of the cloud and the average density measured by the particles themselves.
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT
(2021)
Article
Mechanics
Matthew R. Walker, Marija Vucelja
Summary: The study considers the thermal relaxation of a particle in a piecewise-constant potential landscape and the connection between the Mpemba effect, metastable states, and phase transitions. The Mpemba effect is found to exist even in cases without metastable states and the borders of the areas where the effect happens correspond to either eigenvector changes of direction or to phase transitions. Discussions on the topological aspects of the strong Mpemba effect and proposing the use of topology to search for the Mpemba effect in a physical system are also presented.
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT
(2021)
Article
Mechanics
Guitian He, Guoji Tang, Yan Tian, Maokang Luo, H. Eugene Stanley
Summary: This work investigates the statistical properties of charged-particle motion in a microwave field and a magnetic field with a general direction using a generalized Langevin equation with intrinsic noise. The study focuses on drift velocity, complex susceptibilities, spectral amplification, stationary current density, and power absorption. It is notable that stochastic dynamics of charged particles can be induced by fractional Gaussian noise, and variances and covariances are studied based on relationships between relaxation functions and memory kernel functions.
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT
(2021)
Article
Mechanics
Lennart Dabelow, Stefano Bo, Ralf Eichhorn
Summary: The defining feature of active particles is their constant self-propulsion through the conversion of chemical energy into directed motion, keeping them permanently out of equilibrium. Despite potentially sharing certain equilibrium features, active particles may break the time-reversal symmetry in anharmonic potentials while fulfilling it in harmonic potentials in steady-state trajectories.
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT
(2021)
Article
Mathematics, Applied
Michel Moreau, Bernard Gaveau
Summary: This article investigates the relationship between the evolution of mesoscopic systems and entropy, finding that under certain conditions, mesoscopic systems can be approximated by Markov processes and introduces the concept of Kolmogorov entropy. It demonstrates the connection between Kolmogorov entropy and basic aspects of time, such as irreversibility.
Article
Mechanics
Andrea Baldassarri
Summary: The ABBM model, introduced in the context of hysteresis physics in magnetic materials, has been applied to describe crackling noise phenomena. Through exact calculations of avalanche shapes and comparisons with multi-avalanche shapes, it was found that their normalized shapes are exactly the same. This finding applies to various stochastic processes, demonstrating a correspondence between the excursion and bridge shape distributions.
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT
(2021)
Article
Physics, Multidisciplinary
Etienne Fodor, Takahiro Nemoto, Suriyanarayanan Vaikuntanathan
NEW JOURNAL OF PHYSICS
(2020)
Article
Physics, Multidisciplinary
Oyvind L. Borthne, Etienne Fodor, Michael E. Cates
NEW JOURNAL OF PHYSICS
(2020)
Article
Physics, Multidisciplinary
Etienne Fodor, Michael E. Cates
Summary: The study of thermal heat engines is crucial for establishing equilibrium thermodynamics principles, while the conversion of energy in non-equilibrium systems remains a significant question with potential to illuminate general physical principles. Recent theoretical progress on engines operating with active matter has identified two main classes and strategies for optimization, with limitations in previous studies suggesting the need for a coherent thermodynamic framework far from equilibrium.
Article
Physics, Multidisciplinary
Tomer Markovich, Etienne Fodor, Elsen Tjhung, Michael E. Cates
Summary: This study provides a thermodynamically consistent framework to identify energy exchanges between active systems and their surrounding thermostat, evaluating the cost to sustain the system away from equilibrium, which is related to the irreversibility of the active field dynamics. The research demonstrates the applicability of this approach in popular active field theories and explores how the overall dissipated heat varies with the emerging order.
Article
Physics, Multidisciplinary
Michael E. Cates, Etienne Fodor, Tomer Markovich, Cesare Nardini, Elsen Tjhung
Summary: This article focuses on models with purely diffusive scalar fields and discusses the construction of discretization schemes and their interpretation in the context of continuum limits and entropy production rate calculations. It also explores the effects of spurious drift terms and closely related terms in both temporal and spatial discretization, even in the presence of additive noise.
Review
Physics, Condensed Matter
Etienne Fodor, Robert L. Jack, Michael E. Cates
Summary: This article discusses how active systems evade equilibrium thermodynamics rules by dissipating energy and how stochastic thermodynamics tools can be used to study irreversibility and entropy production in active systems. It also explores the possibility of constructing thermodynamic ensembles out of equilibrium to discover unexpected phase transitions in active matter systems.
ANNUAL REVIEW OF CONDENSED MATTER PHYSICS
(2022)
Article
Chemistry, Physical
Gregory Rassolov, Laura Tociu, Etienne Fodor, Suriyanarayanan Vaikuntanathan
Summary: There is a close relationship between the static structure and dissipation of active systems driven by local non-conservative forces. Liquid-state theories and machine learning tools are used to study this relationship and a neural network is constructed to predict the dissipation rate of the system.
JOURNAL OF CHEMICAL PHYSICS
(2022)
Article
Physics, Multidisciplinary
Alexandra Lamtyugina, Yuqing Qiu, Etienne Fodor, Aaron R. Dinner, Suriyanarayanan Vaikuntanathan
Summary: This letter presents a thermodynamic control principle for structural transitions in a model cytoskeletal network, using methods from large deviation theory. The authors demonstrate that biasing the dynamics with respect to the work done by nonequilibrium components can effectively renormalize the interaction strength between these components, resulting in morphological transitions.
PHYSICAL REVIEW LETTERS
(2022)
Article
Physics, Multidisciplinary
Yuting Irene Li, Rosalba Garcia-Millan, Michael E. Cates, Etienne Fodor
Summary: By using the Boltzmann equation, the dynamics of particle density is established to simulate the non-equilibrium behavior of active matter, revealing the spinodal instability in the Boltzmann dynamics with purely repulsive interactions and demonstrating the broad applicability of the method.
Article
Physics, Fluids & Plasmas
Timothy Ekeh, Etienne Fodor, Suzanne M. Fielding, Michael E. Cates
Summary: The importance of mesoscale fluctuations in flowing amorphous materials is explored in this study. A mean-field elastoplastic model is proposed to investigate the character of power distribution under steady shear flow. The model predicts the suppression of negative power fluctuations near the liquid-solid transition, the existence of a fluctuation relation in limiting regimes, and the replacement of the relation by stretched-exponential power-distribution tails in general. It also uncovers a crossover between two distinct mechanisms for negative power fluctuations in the liquid and the yielding solid phases.
Article
Physics, Fluids & Plasmas
Etienne Fodor, Anton Souslov
Summary: The article discusses the unique properties and applications of odd materials, which exhibit antisymmetric response to perturbations and can convert energy through non-equilibrium activity. It is found that the efficiency of this energy conversion can be close to unity when cyclic deformations are properly designed, providing guidelines for designing more complex engines.
Article
Physics, Fluids & Plasmas
David Martin, Jeremy O'Byrne, Michael E. Cates, Etienne Fodor, Cesare Nardini, Julien Tailleur, Frederic van Wijland
Summary: We study the statistical properties of active Ornstein-Uhlenbeck particles (AOUPs), focusing on steady-state distribution, phase separation mechanisms, and entropy production. The results show deviation from thermal equilibrium limits and recovery of fluctuation-dissipation relations in the small persistence time limit.
Article
Physics, Fluids & Plasmas
Yann-Edwin Keta, Etienne Fodor, Frederic van Wijland, Michael E. Cates, Robert L. Jack
Summary: We analyzed the collective motion that occurs during rare events in systems of active particles, and discussed the associated dynamical phase transition to collective motion. A finite biasing field is needed to induce spontaneous symmetry breaking, and particle alignment is studied both for a two-particle system and for many-particle systems using an optimal-control representation of the biased dynamics. Additionally, a fluctuating hydrodynamic theory is proposed to capture the emergence of polar order in the biased state.
Article
Physics, Fluids & Plasmas
Timothy Ekeh, Michael E. Cates, Etienne Fodor
Article
Physics, Fluids & Plasmas
Takahiro Nemoto, Etienne Fodor, Michael E. Cates, Robert L. Jack, Julien Tailleur
