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A theory for non-equilibrium systems is derived from a maximum entropy approach similar in spirit to the equilibrium theory given by Gibbs. Requiring Hamilton's principle of stationary action to be satisfied on average during a trajectory,…

Statistical Mechanics · Physics 2019-02-04 David M. Rogers , Susan B. Rempe

A principle of maximum entropy is proposed in the context of viscous incompressible flow in Eulerian coordinates. The relative entropy functional, defined over the space of $L^2$ divergence-free velocity fields, is maximized relative to…

Fluid Dynamics · Physics 2024-02-23 Gui-Qiang G. Chen , James Glimm , Hamid Said

The quasi--equilibrium or maximum entropy approximation is applied in order to derive constitutive equations from kinetic models of polymer dynamics. It is shown in general and illustrated for an example how canonical distribution functions…

Statistical Mechanics · Physics 2009-11-07 Patrick Ilg , Iliya V. Karlin , Hans Christian Öttinger

We show that the principle of maximum entropy, a variational method appearing in statistical inference, statistical physics, and the analysis of stochastic dynamical systems, admits a geometric description from gauge theory. Using the…

Mathematical Physics · Physics 2023-01-05 Dalton A R Sakthivadivel

Ideas and formalisms from far-from-equilibrium thermodynamics are ported to the context of stochastic computational processes, via following and extending Tadaki's algorithmic thermodynamics. A Principle of Maximum Algorithmic Caliber is…

Artificial Intelligence · Computer Science 2020-05-12 Ben Goertzel

When a physical system is driven away from equilibrium, the statistical distribution of its dynamical trajectories informs many of its physical properties. Characterizing the nature of the distribution of dynamical observables, such as a…

Statistical Mechanics · Physics 2024-06-19 Jiawei Yan , Grant M. Rotskoff

A path information is defined in connection with different possible paths of irregular dynamic systems moving in its phase space between two points. On the basis of the assumption that the paths are physically differentiated by their…

Statistical Mechanics · Physics 2007-05-23 Qiuping A. Wang

Schr\"odinger bridges have emerged as an enabling framework for unveiling the stochastic dynamics of systems based on marginal observations at different points in time. The terminology "bridge'' refers to a probability law that suitably…

Statistical Mechanics · Physics 2024-03-05 Olga Movilla Miangolarra , Asmaa Eldesoukey , Tryphon T. Georgiou

We show that the naive application of the maximum entropy principle can yield answers which depend on the level of description, i.e. the result is not invariant under coarse-graining. We demonstrate that the correct approach, even for…

Statistical Mechanics · Physics 2007-05-23 Jayanth Banavar , Amos Maritan

Making statistical predictions requires tackling two problems: one must assign appropriate probability distributions and then one must calculate a variety of expected values. The method of maximum entropy is commonly used to address the…

Statistical Mechanics · Physics 2009-11-07 Chih-Yuan Tseng , Ariel Caticha

The principle of maximum entropy is a broadly applicable technique for computing a distribution with the least amount of information possible constrained to match empirical data, for instance, feature expectations. We seek to generalize…

Information Theory · Computer Science 2022-05-30 Kenneth Bogert

We study, in finite volume, a grand canonical version of the McKean-Vlasov equation where the total particle content is allowed to vary. The dynamics is anticipated to minimize an appropriate grand canonical free energy; we make this notion…

Analysis of PDEs · Mathematics 2016-10-27 L. Chayes , H. K. Lei

In 1931, Onsager proposed a variational principle which has become the base of many kinetic equations for non-equilibrium systems. We have been showing that this principle is useful in obtaining approximate solutions for the kinetic…

Soft Condensed Matter · Physics 2020-03-10 Masao Doi , Jiajia Zhou , Yana Di , Xianmin Xu

Maximum entropy distributions with discrete support in $m$ dimensions arise in machine learning, statistics, information theory, and theoretical computer science. While structural and computational properties of max-entropy distributions…

Data Structures and Algorithms · Computer Science 2019-06-04 Damian Straszak , Nisheeth K. Vishnoi

The Principle of Maximum Entropy is a rigorous technique for estimating an unknown distribution given partial information while simultaneously minimizing bias. However, an important requirement for applying the principle is that the…

Information Theory · Computer Science 2026-02-03 Kenneth Bogert , Matthew Kothe

A variation principle for mass transport in solids is derived that recasts transport coefficients as minima of local thermodynamic average quantities. The result is independent of diffusion mechanism, and applies to amorphous and…

Statistical Mechanics · Physics 2018-12-05 Dallas R. Trinkle

We introduce a variational algorithm to estimate the likelihood of a rare event within a nonequilibrium molecular dynamics simulation through the evaluation of an optimal control force. Optimization of a control force within a chosen basis…

Statistical Mechanics · Physics 2021-01-14 Avishek Das , David T. Limmer

The most rigorous physical description of non-equilibrium gas dynamics is rooted in the numerical solution of the Boltzmann equation. Yet, the large number of degrees of freedom and the wide range of both spatial and temporal scales render…

Computational Physics · Physics 2024-10-25 Anthony Chang , Narendra Singh , Marco Panesi

The question of deriving general force/flux relationships that apply out of the linear response regime is a central topic of theories for nonequilibrium statistical mechanics. This work applies an information theory perspective to compute…

Statistical Mechanics · Physics 2019-02-04 David M. Rogers

Maximum entropy (Maxent) models are a class of statistical models that use the maximum entropy principle to estimate probability distributions from data. Due to the size of modern data sets, Maxent models need efficient optimization…

Machine Learning · Statistics 2024-03-12 Gabriel P. Langlois , Jatan Buch , Jérôme Darbon