Abstract

Classical gradient systems have a linear relation between rates and driving forces. In generalized gradient systems we allow for arbitrary relations derived from general non-quadratic dissipation potentials. This paper describes two natural origins for these structures. A first microscopic origin of generalized gradient structures is given by the theory of large-deviation principles. While Markovian diffusion processes lead to classical gradient structures, Poissonian jump processes give rise to cosh-type dissipation potentials. A second origin arises via a new form of convergence, that we call EDP-convergence. Even when starting with classical gradient systems, where the dissipation potential is a quadratic functional of the rate, we may obtain a generalized gradient system in the evolutionary $\Gamma$-limit. As examples we treat (i) the limit of a diffusion equation having a thin layer of low diffusivity, which leads to a membrane model, and (ii) the limit of diffusion over a high barrier, which gives a reaction-diffusion system.

Highlights

  • We consider evolution equations u = V (t, u) that are generated by gradient systems (GS)

  • In all our three examples we find the surprising result that the energy-dissipation principle (EDP)-limit is exactly the entropic GS of the limiting Markov process

  • We consider a compact metric space S and denote by Prob(S) the subset of probability Radon measures on S equipped with the narrow convergence ⇀∗ defined by duality with continuous, bounded functions

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Summary

Introduction

The three main models in this work (i.e. the ODE, the membrane, and the reaction-to-diffusion model in Sections 3.3.2, 4, and 5, respectively) can be seen as Kolmogorov forward equations for naturally associated Markov processes. In all our three examples we find the surprising result that the EDP-limit is exactly the entropic GS of the limiting Markov process This means that applying the described large-deviation principle and taking the limit ε → 0 (either on the level of Markov semigroups or as EDP-convergence for GS) commute, see Figure 1.1. This result appears naturally, if we use representation (1.6) of the rate function I giving. We do not give the full analytical details in terms of estimates and convergences in the proper functional spaces, but rather highlight the structures and manipulations needed to understand the corresponding limit procedures

Classical and generalized gradient systems
Variational principles for gradient systems
The energy-dissipation principle
Examples of generalized gradient structures
Dissipative material models
Nonlinear reaction-diffusion systems
Gradient structures obtained via large deviations
A finite-state Markov process
Linear reaction-diffusion systems
Large deviations for a membrane model
Evolutionary Γ-convergence
EDP-convergence for gradient systems
EDP-convergence for an ODE example
Quadratic energy and dissipation
Entropic energy and C -type dissipation
Entropic energy and quadratic dissipation
The membrane as a thin-layer limit
From diffusion to reaction
A Evaluation of some functionals
Derivation of the potential N
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