An optimization principle for deriving nonequilibrium statistical models of Hamiltonian dynamics
Bruce Turkington

TL;DR
This paper introduces an optimization-based method for deriving nonequilibrium statistical models of Hamiltonian systems, extending classical thermodynamics to nonlinear regimes using a variational principle.
Contribution
It develops a novel optimization framework that derives macroscopic dynamics from microscopic Hamiltonian systems via a Hamilton-Jacobi equation, generalizing linear irreversible thermodynamics.
Findings
Derives reduced models using an optimization principle and statistical estimation.
Extends classical thermodynamics beyond near-equilibrium conditions.
Provides a nonlinear relation between thermodynamic forces and fluxes.
Abstract
A general method for deriving closed reduced models of Hamiltonian dynamical systems is developed using techniques from optimization and statistical estimation. As in standard projection operator methods, a set of resolved variables is selected to capture the slow, macroscopic behavior of the system, and the family of quasi-equilibrium probability densities on phase space corresponding to these resolved variables is employed as a statistical model. The macroscopic dynamics of the mean resolved variables is determined by optimizing over paths of these probability densities. Specifically, a cost function is introduced that quantifies the lack-of-fit of such paths to the underlying microscopic dynamics; it is an ensemble-averaged, squared-norm of the residual that results from submitting a path of trial densities to the Liouville equation. The evolution of the macrostate is estimated by…
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