The development and quantitative validation of complex nonlinear differential equation models
is a difficult task that requires the support by numerical methods for sensitivity analysis,
parameter estimation, and the optimal design of experiments. The talk first presents particularly
efficient "simultaneous" boundary value problems methods for parameter estimation in
nonlinear differential algebraic equations, which are based on constrained
Gauss-Newton-type methods and a time domain decomposition by multiple shooting.
They include a numerical analysis of the well-posedness of the problem and an assessment
of the error of the resulting parameter estimates. Based on these approaches, efficient
optimal control methods for the determination of one, or several complementary, optimal
experiments are developed, which maximize the information gain subject to constraints
such as experimental costs and feasibility, the range of model validity, or further technical
constraints.
Special emphasis is placed on issues of robustness, i.e. how to reduce the sensitivity of the
problem solutions with respect to uncertainties - such as outliers in the measurements for
parameter estimation, and in particular the dependence of optimum experimental designs on
the largely unknown values of the model parameters. New numerical methods will be presented,
and applications will be discussed that arise in satellite orbit determination, chemical
reaction kinetics, enzyme kinetics and robotics. They indicate a wide scope of applicability of
the methods, and an enormous potential for reducing the experimental effort and improving
the statistical quality of the models.
(Based on joint work with H. G. Bock, S. Koerkel, and J. P. Schloeder.)