We consider numerical approximations of stochastic differential equations by
the Euler method. In the case where the SDE is elliptic or hypoelliptic, we
show a weak backward error analysis result in the sense that the generator
associated with the numerical solution coincides with the solution of a
modified Kolmogorov equation up to high order terms with respect to the
stepsize.
We prove a Nekhoroshev type theorem for the nonlinear Schr\"odinger equation
$$ iu_t=-\Delta u+V\star u+\partial_{\bar u}g(u,\bar u)\, \quad x\in \T^d, $$
where $V$ is a typical smooth potential and $g$ is analytic in both variables.
More precisely we prove that if the initial datum is analytic in a strip of
width $\rho>0$ with a bound on this strip equals to $\eps$ then, if $\eps$ is
small enough, the solution of the nonlinear Schr\"odinger equation above
remains analytic in a strip of width $\rho/2$ and bounded on this strip by
$C\eps$ during very long time of order $ \eps^{-\alpha|\ln
We consider a wide class of semi linear Hamiltonian partial differential
equa- tions and their approximation by time splitting methods. We assume that
the nonlinearity is polynomial, and that the numerical tra jectory remains at
least uni- formly integrable with respect to an eigenbasis of the linear
operator (typically the Fourier basis). We show the existence of a modified
interpolated Hamiltonian equation whose exact solution coincides with the
discrete flow at each time step over a long time depending on a non resonance
condition satisfied by the stepsize.
We consider a general class of infinite dimensional reversible differential
systems. Assuming a non resonance condition on the linear frequencies, we
construct for such systems almost invariant pseudo norms that are closed to
Sobolev-like norms. This allows us to prove that if the Sobolev norm of index
$s$ of the initial data $z_0$ is sufficiently small (of order $\epsilon$) then
the Sobolev norm of the solution is bounded by $2\epsilon$ during very long
time (of order $\epsilon^{-r}$ with $r$ arbitrary).