2020
DOI: 10.1214/20-ejp439
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Homogenisation for anisotropic kinetic random motions

Abstract: We introduce a class of kinetic and anisotropic random motions px σ t , v σ t qtě0 on the unit tangent bundle T 1 M of a general Riemannian manifold pM, gq, where σ is a positive parameter quantifying the amount of noise affecting the dynamics. As the latter goes to infinity, we then show that the time rescaled process px σ σ 2 t qtě0 converges in law to an explicit anisotropic Brownian motion on M. Our approach is essentially based on the strong mixing properties of the underlying velocity process and on roug… Show more

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Cited by 4 publications
(7 citation statements)
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“…This statement generalizes Proposition 1.1 of [Per18] to the present infinite dimensional setting. The above description of the invariant measure µ as an image measure under the projection map actually coincides with the finite dimensional description given in the latter reference.…”
Section: Brownian Motion On a Hilbert Spheresupporting
confidence: 79%
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“…This statement generalizes Proposition 1.1 of [Per18] to the present infinite dimensional setting. The above description of the invariant measure µ as an image measure under the projection map actually coincides with the finite dimensional description given in the latter reference.…”
Section: Brownian Motion On a Hilbert Spheresupporting
confidence: 79%
“…The statement of Proposition 2.5 follows then from the conclusion of Dedecker and Merlevède convergence result. The identification of the covariance (7) is a consequence of the corresponding statement, Proposition 3.4, in the finite dimensional setting of [Per18].…”
Section: By Induction We Getmentioning
confidence: 89%
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“…A self-contained proof by the same author appeared later in [Li16]. This result was generalised by Angst, Bailleul and Tardif [ABT15] and the author [Per20]. Kinetic Brownian motion has been given different names in the literature, for instance velocity spherical Brownian motion by Baudoin and Tardif [BT18] or circular Langevin diffusion by Franchi [Fra15] in the context of heat kernels.…”
Section: Introductionmentioning
confidence: 96%