Generalization of the Dynamical Lack-of-Fit Reduction from GENERIC to GENERIC

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作者
Michal Pavelka
Václav Klika
Miroslav Grmela
机构
[1] Charles University,Faculty of Mathematics and Physics, Mathematical Institute
[2] Czech Technical University in Prague,Department of Mathematics – FNSPE
[3] École Polytechnique de Montréal,undefined
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Best fit Lagrangian reduction; GENERIC; Hamiltonian mechanics; Gradient dynamics; Hamilton–Jacobi equation;
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摘要
The lack-of-fit statistical reduction, developed and formulated first by Bruce Turkington, is a general method taking Liouville equation for probability density (detailed level) and transforming it to reduced dynamics of projected quantities (less detailed level). In this paper the method is generalized. The Hamiltonian Liouville equation is replaced by an arbitrary Hamiltonian evolution combined with gradient dynamics (GENERIC), the Boltzmann entropy is replaced by an arbitrary entropy, and the kinetic energy by an arbitrary energy. The gradient part is a generalized gradient dynamics generated by a dissipation potential. The reduced evolution of the projected state variables is shown to preserve the GENERIC structure of the original (detailed level) evolution. The dissipation potential is obtained by solving a Hamilton–Jacobi equation. In summary, the lack-of-fit reduction can start with GENERIC and obtain GENERIC for the reduced state variables.
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页码:19 / 52
页数:33
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