  We present a novel acceleration method for the solution of parametric ODEs by
single-step implicit solvers by means of greedy kernel-based surrogate models.
In an offline phase, a set of trajectories is precomputed with a high-accuracy
ODE solver for a selected set of parameter samples, and used to train a kernel
model which predicts the next point in the trajectory as a function of the last
one. This model is cheap to evaluate, and it is used in an online phase for new
parameter samples to provide a good initialization point for the nonlinear
solver of the implicit integrator. The accuracy of the surrogate reflects into
a reduction of the number of iterations until convergence of the solver, thus
providing an overall speedup of the full simulation. Interestingly, in addition
to providing an acceleration, the accuracy of the solution is maintained, since
the ODE solver is still used to guarantee the required precision. Although the
method can be applied to a large variety of solvers and different ODEs, we will
present in details its use with the Implicit Euler method for the solution of
the Burgers equation, which results to be a meaningful test case to demonstrate
the method's features.
