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Rough Path Theory to approximate Random Dynamical Systems

Probability 2020-02-25 v1 Dynamical Systems

Abstract

We consider the rough differential equation dY=f(Y)d\omdY=f(Y)d\bm \om where \om=(ω,\bbomega)\bm \om=(\omega,\bbomega) is a rough path defined by a Brownian motion ω\omega on \RRm\RR^m. Under the usual regularity assumption on ff, namely fCb3(\RRd,\RRd×m)f\in C^3_b (\RR^d, \RR^{d\times m}), the rough differential equation has a unique solution that defines a random dynamical system ϕ0\phi_0. On the other hand, we also consider an ordinary random differential equation dYδ=f(Yδ)dω\dedY_\delta=f(Y_\delta)d\omega_\de, where ω\de\omega_\de is a random process with stationary increments and continuously differentiable paths that approximates ω\omega. The latter differential equation generates a random dynamical system ϕδ\phi_\delta as well. We show the convergence of the random dynamical system ϕδ\phi_\delta to ϕ0\phi_0 for δ0\delta\to 0 in H\"older norm.

Keywords

Cite

@article{arxiv.2002.10425,
  title  = {Rough Path Theory to approximate Random Dynamical Systems},
  author = {Hongjun Gao and María J. Garrido-Atienza and Anhui Gu and Kening Lu and Björn Schmalfuss},
  journal= {arXiv preprint arXiv:2002.10425},
  year   = {2020}
}

Comments

23 pages

R2 v1 2026-06-23T13:52:03.557Z