6 citations · 6 across the 6 of their papers we have counts for
14 papers
Convergence of the stochastic Navier-Stokes- solutions toward the stochastic Navier-Stokes solutions
Jad Doghman, Ludovic Goudenège
Loosely speaking, the Navier-Stokes- model and the Navier-Stokes equations differ by a spatial filtration parametrized by a scale denoted . Starting from a strong two-dimensi…
Computing XVA for American basket derivatives by Machine Learning techniques
Ludovic Goudenege, Andrea Molent, Antonino Zanette
Total value adjustment (XVA) is the change in value to be added to the price of a derivative to account for the bilateral default risk and the funding costs. In this paper, we comp…
Numerical and convergence analysis of the stochastic Lagrangian averaged Navier-Stokes equations
Jad Doghman, Ludovic Goudenège
The primary emphasis of this work is the development of a finite element based space-time discretization for solving the stochastic Lagrangian averaged Navier-Stokes (LANS-) equ…
Moving average options: Machine Learning and Gauss-Hermite quadrature for a double non-Markovian problem
Ludovic Goudenège, Andrea Molent, Antonino Zanette
Evaluating moving average options is a tough computational challenge for the energy and commodity market as the payoff of the option depends on the prices of a certain underlying o…
Revisiting the framework for intermittency in Lagrangian stochastic models for turbulent flows: a way to an original and versatile numerical approach
Roxane Letournel, Ludovic Goudenège, Rémi Zamansky +2
The characterization of intermittency in turbulence has its roots in the K62 theory, and if no proper definition is to be found in the literature, statistical properties of intermi…
Ergodicity of stochastic Cahn-Hilliard equations with logarithmic potentials driven by degenerate or nondegenerate noises
Ludovic Goudenège, Bin Xie
We study the asymptotic properties of the stochastic Cahn-Hilliard equation with the logarithmic free energy by establishing different dimension-free Harnack inequalities according…