The engineering systems our clients have to handle are subject to uncertainty. On the one hand, one typically cannot control the parameters of the probabilistic model for the load exerted on the system of interest. On the other hand, during the design process, our clients often have control over the parameter of the probabilistic model for the resistance side. We help our clients in determining the design parameters of their systems such that the reliability or cost-effectiveness is optimized conditional on the required reliability constraints.
Such an optimization under uncertainty is computationally usually considerably more demanding than a standard optimization. Our algorithms for stochastic optimization are designed for both robustness and efficiency. If the involved computational costs are too large, we help our clients in setting up reasonable surrogate models that can be used to tackle the optimization under uncertainty.