With the so called continuous stochastic gradient (CSG) method, recently a tool has been developed, which allows to efficiently solve density based topology optimization problems which are subject to random data.

In this talk it is demonstrated how the CSG idea to can be incorporated into classical solution methods for density based topology optimization problems such as the well known optimality criteria method (OCM) or the method of moving asymptotes (MMA).

The ease of use as well as the practical efficiency of the resulting methods will be demonstrated by a couple of examples from structural optimization covering, among others, stochastic loads and stochastic damage.