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20082020
most citedErgodicity of Markov Semigroups with Hörmander type generators in Infinite Dimensions

1 citations · 1 across the 5 of their papers we have counts for

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7 papers

math.AP2020

Asymptotics for optimal controls for horizontal mean curvature flow

Nicolas Dirr, Federica Dragoni, Raffaele Grande

The solutions to surface evolution problems like mean curvature flow can be expressed as value functions of suitable stochastic control problems, obtained as limit of a family of r…

math.AP2019

- convergence and homogenisation for a class of degenerate functionals

Nicolas Dirr, Federica Dragoni, Paola Mannucci +1

This paper is on -convergence for degenerate integral functionals related to homogenisation problems in the Heisenberg group. Here both the rescaling and the notion of invarianc…

math.AP2018

Starshapedeness for fully-nonlinear equations in Carnot groups

Federica Dragoni, Nicola Garofalo, Paolo Salani

In this paper we establish the starshapedness of the level sets of the capacitary potential of a large class of fully-nonlinear equations for condensers in Carnot groups, once a na…

math.AP2017

Stochastic homogenization for functionals with anisotropic rescaling and non-coercive Hamilton-Jacobi equations

Nicolas Dirr, Federica Dragoni, Paola Mannucci +1

We study the stochastic homogenization for a Cauchy problem for a first-order Hamilton-Jacobi equation whose operator is not coercive w.r.t. the gradient variable. We look at Hamil…

math.AP2017

Ergodic Mean Field Games with Hörmander diffusions

Federica Dragoni, Ermal Feleqi

We prove existence of solutions for a class of systems of subelliptic PDEs arising from Mean Field Game systems with Hörmander diffusion. These results are motivated by the feedbac…

math.AP20101 cited

Ergodicity of Markov Semigroups with Hörmander type generators in Infinite Dimensions

F. Dragoni, V. Kontis, B. Zegarlinski

We develop an effective strategy for proving strong ergodicity of (nonsymmetric) Markov semigroups associated to Hörmander type generators when the underlying configuration space i…