Machine learning meets set-based design: A practical approach to overcoming complexity in vehicle design and simulation
In a new technical paper, published at NAFEMS World Congress 2025, we explore how advanced ML techniques can enhance set-based design so automotive engineers can understand and navigate high-dimensional design spaces more efficiently.
This technical paper explores how set-based design methodologies, integrated with advanced machine learning techniques, can transform automotive system design. While set-based design enables early exploration of diverse alternatives, the increasing complexity and rapid pace of the automotive industry pose challenges for traditional methods. This research highlights how machine learning (ML) enhances efficiency and decision-making, offering deeper insights into complex design spaces.
Emphasizing practical applications, the paper demonstrates how ML can reduce dependence on costly simulations, minimize schedule risks, and improve interdisciplinary collaboration. By intelligently predicting outcomes with limited data, engineers can address multifaceted design challenges more effectively. Download the full paper to learn how these strategies can accelerate your design processes and unlock innovation here.
