These slides were presented at the NAFEMS World Congress 2025, held in Salzburg, Austria from May 19–22, 2025.

Abstract

The European automotive industry faces immense pressure to stay competitive amid growing demands for innovation, stricter safety standards, and ambitious sustainability goals. Development cycles remain longer than those of international competitors, delaying market entry. Limited budgets and resources further amplify the need to replace physical prototypes with digital simulations and virtual testing. This requires faster, more accurate simulation workflows without compromising quality. To address these challenges, AI is integrated into CAE workflows to accelerate simulations, improve accuracy, and reduce redundant efforts. The solution relies on three interconnected pillars that streamline the process and demonstrate how AI can be successfully implemented in practice. The first pillar is structured data management, where simulation and test data from tools such as ANSYS, NASTRAN, and Abaqus are centralized into a structured platform. We show how valuable legacy data'”often scattered and underutilized'”can be accessed, reused, and prepared for AI applications in a structured and automated way. The second pillar involves the use of small, task-specific AI models for pre-screening. Unlike large, generic machine learning models, these smaller, highly focused models predict outcomes based on historical simulation and test data. In our presentation, we will demonstrate how these AI models serve as an intelligent pre-screening mechanism, identifying the most promising design solutions early in the process. This allows engineers to focus computational resources effectively, reducing unnecessary simulations and redundant iterations. Participants will see how straightforward it can be to train and deploy these models with existing tools and workflows. The third pillar focuses on continuous improvement and collaboration. AI-generated predictions, along with the trained models, are integrated back into the data management system, creating a continuous improvement loop. We will showcase how engineers can reuse pre-trained models for new tasks without the need for retraining or additional data preparation. This approach significantly reduces the number of required simulations while fostering collaboration: teams across projects can access validated models, apply them to new challenges, and build upon previous insights. Our practical examples will prove that AI implementation is not a 'œblack box,' but an accessible and tangible solution for engineering teams. In this presentation, we will go beyond describing the solution. We will demonstrate its practical implementation step by step, proving that integrating AI into CAE workflows is not complex or exclusive to experts. By showcasing real-world examples and a concrete workflow, we will highlight how AI tools can be effectively deployed to centralize data, train task-specific models, and create a self-improving simulation ecosystem. Participants will see firsthand how AI empowers engineers to accelerate design processes, optimize resources, and deliver innovative results. The key message: applying AI is achievable and accessible for every team willing to leverage their data effectively. The Impact This AI-driven approach accelerates R&D processes by reducing unnecessary simulations, streamlining workflows, and enabling engineers to focus on high-value tasks like design exploration and system optimization. The result is shorter development cycles, greater resource efficiency, and improved decision-making without compromising quality. While AI does not replace traditional simulations, it serves as a powerful complement, enhancing speed, precision, and collaboration. By the end of the presentation, participants will not only understand the value of AI in CAE but also leave with actionable insights to implement AI-driven workflows within their own teams'”proving that AI is not magic but a practical tool for transforming CAE into a strategic enabler of innovation.