2020
DOI: 10.1016/j.anihpc.2020.01.006
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Lipschitz regularity for viscous Hamilton-Jacobi equations with Lp terms

Abstract: We provide Lipschitz regularity for solutions to viscous time-dependent Hamilton-Jacobi equations with right-hand side belonging to Lebesgue spaces. Our approach is based on a duality method, and relies on the analysis of the regularity of the gradient of solutions to a dual (Fokker-Planck) equation. Here, the regularizing effect is due to the non-degenerate diffusion and coercivity of the Hamiltonian in the gradient variable.

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Cited by 25 publications
(93 citation statements)
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“…Finally, our technique does not apply to the parabolic counterpart of (M). In this direction, some results based on rather different duality methods developed in [12] to get Lipschitz regularity, have been obtained in [13]. 2 will denote the partial derivative in the i-th direction, the gradient, and the Hessian operator respectively.…”
Section: Introductionmentioning
confidence: 99%
“…Finally, our technique does not apply to the parabolic counterpart of (M). In this direction, some results based on rather different duality methods developed in [12] to get Lipschitz regularity, have been obtained in [13]. 2 will denote the partial derivative in the i-th direction, the gradient, and the Hessian operator respectively.…”
Section: Introductionmentioning
confidence: 99%
“…In [12], they are studied in connection with space-fractional PDEs using arguments inspired by [28,29]. These spaces are natural in the context of parabolic PDEs and the corresponding L p theory is crucial in the study of parabolic regularity properties even for equations with divergence-type terms (see [5,11,13,38,41]).…”
Section: Parabolic Sobolev Spacesmentioning
confidence: 99%
“…In this section, we prove gradient bound for classical solution to (16). The method implemented here is based on the so-called nonlinear adjoint method (see [11,22] and [12, Proposition 3.6]), that is on testing the Hamilton-Jacobi equation against the (classical) solution to…”
Section: Gradient Boundmentioning
confidence: 99%
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