Stochastic homogenization of A-convex gradient flows

Research output: Contribution to journalResearch articleContributedpeer-review

Contributors

  • Martin Heida - , Weierstrass Institute for Applied Analysis and Stochastics (Author)
  • Stefan Neukamm - , Technical University of Munich (Author)
  • Mario Varga - , Technical University of Munich (Author)

Abstract

In this paper we present a stochastic homogenization result for a class of Hilbert space evolutionary gradient systems driven by a quadratic dissipation potential and a Lambda-convex energy functional featuring random and rapidly oscillating coefficients. Specific examples included in the result are Allen-Cahn type equations and evolutionary equations driven by the p-Laplace operator with p is an element of (1, infinity). The homogenization procedure we apply is based on a stochastic two-scale convergence approach. In particular, we define a stochastic unfolding operator which can be considered as a random counterpart of the well-established notion of periodic unfolding. The stochastic unfolding procedure grants a very convenient method for homogenization problems defined in terms of (Lambda-)convex functionals.

Details

Original languageEnglish
Pages (from-to)427-453
Number of pages27
JournalDiscrete and continuous dynamical systems-Series s
Volume14
Issue number1
Publication statusPublished - Jan 2021
Peer-reviewedYes
Externally publishedYes

External IDs

Scopus 85098882990

Keywords

Keywords

  • Stochastic homogenization, stochastic unfolding, two-scale convergence, gradient system, 2-SCALE HOMOGENIZATION, GAMMA-CONVERGENCE, RANDOM-WALKS, HILBERT, SPACES

Library keywords