Development of a System for Regression Model Enhancement Utilizing Shape Generation AI

This study proposes a method to enhance regression models by shape generation AI. The approach focuses on automatically identifying regions within the design space where the model’s prediction accuracy is low. Once these regions are identified, new and diverse sample shapes are automatically generated by the shape generation AI and incorporated into the training dataset. The regression model is then retrained to improve its performance. By iteratively repeating this cycle of exploration, shape (FE mesh) generation, and model updating, the model’s reliability and accuracy across the entire design space are progressively enhanced. This method addresses data sparsity issues common in complex design tasks and enables better generalization to underrepresented regions. The effectiveness of the proposed system was demonstrated through a case study involving hood outer panels in automotive design. The results showed that adding AI-generated shapes improved prediction accuracy, particularly in regions initially exhibiting high uncertainty or poor performance. These findings suggest that the system can effectively enhance regression models for complex shape prediction tasks. Overall, the proposed approach offers a scalable and efficient solution for advancing predictive modeling in automotive design and other engineering fields where accurate predictions of complex geometries are essential. By integrating shape generation AI with uncertainty-driven data augmentation and retraining, this method autonomously improves regression models.