  Ordinary differential equations (ODEs) are widely used to model biological,
(bio-)chemical and technical processes. The parameters of these ODEs are often
estimated from experimental data using ODE-constrained optimisation. This
article proposes a simple simulation-based approach for solving optimisation
problems with steady state constraints relying on an ODE. This simulation-based
optimisation method is tailored to the problem structure and exploits the local
geometry of the steady state manifold and its stability properties. A
parameterisation of the steady state manifold is not required. We prove local
convergence of the method for locally strictly convex objective functions.
Effciency and reliability of the proposed method are demonstrated in two
examples. The proposed method demonstrated better convergence properties than
existing general purpose methods and a significantly higher number of converged
starts per time.
