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math.NA2026

Post-processed frozen-flow methods for the long time sampling of ergodic dynamics on Riemannian manifolds

Adrien Busnot Laurent, Sébastien Macé

In this work, we propose a novel intrinsic approach to the approximation of ergodic SDEs on Riemannian manifolds, which include Riemannian Langevin dynamics. In opposition to the s…

math.NA2026

Control theory and splitting methods

Karine Beauchard, Adrien Busnot Laurent, Frédéric Marbach

Our goal is to highlight some deep connections between numerical splitting methods and control theory. We consider evolution equations of the form , wher…

math.NA2026

Derivation of optimal stochastic Runge-Kutta methods with exotic and decorated Butcher series for the weak integration of stochastic dynamics

Adrien Busnot Laurent, Kristian Debrabant, Anne Kværnø

The design of numerical integrators for solving stochastic dynamics with high weak order relies on tedious calculations and is subject to a high number of order conditions. The ori…

math.NA2025

Preconditioning for the high-order sampling of the invariant distribution of parabolic semilinear SPDEs

Charles-Edouard Bréhier, Adrien Busnot Laurent, Arnaud Debussche +1

For a class of ergodic parabolic semilinear stochastic partial differential equations (SPDEs) with gradient structure, we introduce a preconditioning technique and design high-orde…

math.NA2025

High order integration of stochastic dynamics on Riemannian manifolds with frozen flow methods

Eugen Bronasco, Adrien Busnot Laurent, Baptiste Huguet

We present a new class of numerical methods for solving stochastic differential equations with additive noise on general Riemannian manifolds with high weak order of accuracy. In o…

math.NA2024

Hopf algebra structures for the backward error analysis of ergodic stochastic differential equations

Eugen Bronasco, Adrien Laurent

While backward error analysis does not generalise straightforwardly to the strong and weak approximation of stochastic differential equations, it extends for the sampling of ergodi…