Abstract
To improve the performance prediction accuracy of surrogate models for exterior panels, a cycle that explores and detect a lack of training data in design space, creates new training data using 3D shape generation AI and CAE for the detected area and iteratively update the surrogate model is established. The process was applied to the surrogate model of hood outer rigidity performance and the prediction accuracy is improved．
