Using Optimus, the entire simulation process can be automated, and different design studies or intelligent optimization algorithms can be applied. The cost factor for the high-strength material can easily be changed and an appropriate design can be selected according to the desired saving of mass and cost.

This case study presents a machine-learning algorithm that addresses the challenge by guiding numerical experiments at different fidelity levels, adaptively selecting sampling locations, and optimizing the design using Optimus.