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Depending on context, the term entropy is used for a thermodynamic quantity, a~measure of available choice, a quantity to measure information, or, in the context of statistical inference, a maximum configuration predictor. For systems in…

Statistical Mechanics · Physics 2018-11-14 Rudolf Hanel , Stefan Thurner

The principle of maximum entropy is a broadly applicable technique for computing a distribution with the least amount of information possible while constrained to match empirically estimated feature expectations. However, in many real-world…

Machine Learning · Computer Science 2022-08-16 Kenneth Bogert , Yikang Gui , Prashant Doshi

The first aims of this work are to endorse the advent of finitely additive set functions as equilibrium states and the possibility to replace the metric entropy by an upper semi-continuous map associated to a general variational principle.…

Dynamical Systems · Mathematics 2022-04-07 Andrzej Bis , Maria Carvalho , Miguel Mendes , Paulo Varandas

When modelling driven steady states of matter, it is common practice either to choose transition rates arbitrarily, or to assume that the principle of detailed balance remains valid away from equilibrium. Neither of those practices is…

Statistical Mechanics · Physics 2009-11-10 R. M. L. Evans

Optimal prediction approximates the average solution of a large system of ordinary differential equations by a smaller system. We present how optimal prediction can be applied to a typical problem in the field of molecular dynamics, in…

Mathematical Physics · Physics 2008-11-15 Benjamin Seibold

A variant of continuous nonequilibrium thermodynamic theory based on the postulate of the scale invariance of the local relation between generalized fluxes and forces has been proposed. This single postulate replaces the assumptions on…

Statistical Mechanics · Physics 2015-06-05 Leonid M. Martyushev , V. D. Seleznev

Variational approximations are increasingly based on gradient-based optimization of expectations estimated by sampling. Handling discrete latent variables is then challenging because the sampling process is not differentiable. Continuous…

Machine Learning · Computer Science 2021-02-09 Tomasz Kuśmierczyk , Arto Klami

A systematic approach to finding variational approximation in an otherwise intractable non-conjugate model is to exploit the general principle of convex duality by minorizing the marginal likelihood that renders the problem tractable. While…

Statistics Theory · Mathematics 2020-10-27 Indrajit Ghosh , Anirban Bhattacharya , Debdeep Pati

This study examines a new formulation of non-equilibrium thermodynamics, which gives a conditional derivation of the ``maximum entropy production'' (MEP) principle for flow and/or chemical reaction systems at steady state. The analysis uses…

Populations and Evolution · Quantitative Biology 2009-08-25 Robert K. Niven

Extreme value theory for chaotic dynamical systems is a rapidly expanding area of research. Given a system and a real function (observable) defined on its phase space, extreme value theory studies the limit probabilistic laws obeyed by…

Dynamical Systems · Mathematics 2015-05-28 Mark P. Holland , Renato Vitolo , Pau Rabassa , Alef E. Sterk , Henk W. Broer

Using cyclic shear to drive a two dimensional granular system, we determine the structural characteristics for different inter-particle friction coefficients. These characteristics are the result of a competition between mechanical…

Soft Condensed Matter · Physics 2021-01-06 Xulai Sun , Walter Kob , Raphael Blumenfeld , Hua Tong , Yujie Wang , Jie Zhang

We discuss averaged turbulence modeling of multi-scales of length for an incompressible Newtonian fluid, with the help of the maximum information principle. We suppose that there exists a function basis to decompose the turbulent…

Fluid Dynamics · Physics 2010-09-10 L. Tao , M. Ramakrishna

The Principle of Insufficient Reason (PIR) assigns equal probabilities to each alternative of a random experiment whenever there is no reason to prefer one over the other. The Maximum Entropy Principle (MaxEnt) generalizes PIR to the case…

Machine Learning · Statistics 2021-11-25 Dominik Janzing

We provide a framework for high-order discretizations of nonlinear scalar convection-diffusion equations that satisfy a discrete maximum principle. The resulting schemes can have arbitrarily high order accuracy in time and space, and can be…

Numerical Analysis · Mathematics 2021-09-20 Manuel Quezada de Luna , David I. Ketcheson

Distribution functions of many static transport equations are found using the Maximum Entropy Principle. The equations of constraint which contain the relevant dynamical information are simply the low-lying moments of the distributions.…

Statistical Mechanics · Physics 2020-04-22 J. A. Secrest , J. M. Conroy , H. G. Miller

Maximum-entropy distributions are shown to appear in the probability calculus as approximations of a model by exchangeability or a model by sufficiency, the former model being preferable. The implications of this fact are discussed,…

Data Analysis, Statistics and Probability · Physics 2017-06-27 P. G. L. Porta Mana

The Principle of Maximum Entropy, a powerful and general method for inferring the distribution function given a set of constraints, is applied to deduce the overall distribution of 3D plasmoids (flux ropes/tubes) for systems where resistive…

High Energy Astrophysical Phenomena · Physics 2018-01-22 Manasvi Lingam , Luca Comisso

Although coarse-grained models have been widely used to explain exotic phenomena in complex fluids, such as droplet formation in living cells, these conventional approaches often fail to capture the intricate microscopic degrees of freedom…

Soft Condensed Matter · Physics 2025-06-13 Masanari Shimada , Tetsuya J. Kobayashi

Entropy serves as a central observable which indicates uncertainty in many chemical, thermodynamical, biological and ecological systems, and the principle of the maximum entropy (MaxEnt) is widely supported in natural science. Recently,…

Physics and Society · Physics 2015-06-03 Bin Xu , Hongen Zhang , Zhijian Wang , Jianbo Zhang

This work presents mixed variational flows (MixFlows), a new variational family that consists of a mixture of repeated applications of a map to an initial reference distribution. First, we provide efficient algorithms for i.i.d. sampling,…

Machine Learning · Statistics 2025-06-03 Zuheng Xu , Naitong Chen , Trevor Campbell
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