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Main Authors: Davini, Andrea, Saona, Raimundo, Ziliotto, Bruno
Format: Preprint
Published: 2024
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Online Access:https://arxiv.org/abs/2406.18404
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author Davini, Andrea
Saona, Raimundo
Ziliotto, Bruno
author_facet Davini, Andrea
Saona, Raimundo
Ziliotto, Bruno
contents We prove stochastic homogenization for a class of non-convex and non-coercive first-order Hamilton-Jacobi equations in a finite-range-dependence environment for Hamiltonians that can be expressed by a max-min formula. Exploiting the representation of solutions as value functions of differential games, we develop a game-theoretic approach to homogenization. We furthermore extend this result to a class of Lipschitz Hamiltonians that need not admit a global max-min representation. Our methods allow us to get a quantitative convergence rate for solutions with linear initial data toward the corresponding ones of the effective limit problem.
format Preprint
id arxiv_https___arxiv_org_abs_2406_18404
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Stochastic Homogenization of HJ Equations: a Differential Game Approach
Davini, Andrea
Saona, Raimundo
Ziliotto, Bruno
Analysis of PDEs
35B27, 35F21, 91A23, 60K37
We prove stochastic homogenization for a class of non-convex and non-coercive first-order Hamilton-Jacobi equations in a finite-range-dependence environment for Hamiltonians that can be expressed by a max-min formula. Exploiting the representation of solutions as value functions of differential games, we develop a game-theoretic approach to homogenization. We furthermore extend this result to a class of Lipschitz Hamiltonians that need not admit a global max-min representation. Our methods allow us to get a quantitative convergence rate for solutions with linear initial data toward the corresponding ones of the effective limit problem.
title Stochastic Homogenization of HJ Equations: a Differential Game Approach
topic Analysis of PDEs
35B27, 35F21, 91A23, 60K37
url https://arxiv.org/abs/2406.18404