  We present a method for the identification of continuous, spatiotemporal
dynamics from experimental data. We use a model in the form of a partial
differential equation and formulate an optimization problem for its estimation
from data. The solution is found as a multivariate nonlinear regression problem
using the ACE-algorithm. The procedure is successfully applied to data,
obtained by simulation of the Swift-Hohenberg equation. There are no
restrictions on the dimensionality of the investigated system, allowing for the
analysis of high-dimensional chaotic as well as transient dynamics. The demands
on the experimental data are discussed as well as the sensitivity of the method
towards noise.
