2018
DOI: 10.1515/jnet-2017-0005
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Gradient Dynamics and Entropy Production Maximization

Abstract: Abstract:We compare two methods for modeling dissipative processes, namely gradient dynamics and entropy production maximization. Both methods require similar physical inputs-how energy (or entropy) is stored and how it is dissipated. Gradient dynamics describes irreversible evolution by means of dissipation potential and entropy, it automatically satisfies Onsager reciprocal relations as well as their nonlinear generalization (Maxwell-Onsager relations), and it has statistical interpretation. Entropy producti… Show more

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Cited by 13 publications
(9 citation statements)
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“…This is guaranteed for instance for convex dissipation potentials, but also non-convexity far from the origin (equilibrium) be taken into account [57]. Moreover, it is in close relation to the method of entropy production maximization [58]. Gradient dynamics plays a key role when formulating dissipation in the GENERIC framework.…”
Section: Dissipation Potentialmentioning
confidence: 90%
“…This is guaranteed for instance for convex dissipation potentials, but also non-convexity far from the origin (equilibrium) be taken into account [57]. Moreover, it is in close relation to the method of entropy production maximization [58]. Gradient dynamics plays a key role when formulating dissipation in the GENERIC framework.…”
Section: Dissipation Potentialmentioning
confidence: 90%
“…Appendix B, but it can also be non-convex without violating the second law [32]. Gradient dynamics is in close relation with entropy production maximization [33,34,35] and the steepest entropy ascent (SEA) [36].…”
Section: Generic On the Upper Levelmentioning
confidence: 95%
“…In this picture, reversible part of the dynamics is governed by a Hamiltonian system, [29] whereas irreversible part is governed by a gradient system. We cite a recent study on the gradient systems and the entropy maximization [30]. In literature, this coupling is also called as metriplectic [31,32].…”
Section: A Note On the Geometric Foundations Of The 3d-qg Modelmentioning
confidence: 99%