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

Abstract

The convergence of High-Performance Computing (HPC) and Artificial Intelligence (AI) is revolutionizing engineering simulation, marking the dawn of a data-centric era in innovation and optimization. This paper proposes a comprehensive framework for simulation data management tailored to harness the power of AI for engineering applications. The ability to efficiently manage, analyze, and extract insights from high-quality simulation data is pivotal to realizing the transformative potential of this convergence. Central to the proposed approach is the establishment of a centralized data repository, designed to unify diverse datasets, including experimental data, numerical simulations, and third-party resources. Such a repository serves as the foundation for streamlined data organization and access, enabling engineers to manage the growing complexity of simulation data effectively. Leveraging AI-powered analytics, advanced machine learning and deep learning algorithms can be applied to these datasets to identify patterns, uncover insights, and drive data-driven decision-making. This capability significantly accelerates the design and optimization processes while reducing reliance on trial-and-error methodologies. Custom machine learning models play a critical role in this framework, offering tailored solutions for predicting performance metrics, optimizing designs, and automating routine engineering tasks. For instance, in the automotive sector, AI-driven simulations can predict vehicle performance, fuel efficiency, and safety parameters with unprecedented accuracy. This not only shortens development cycles but also enhances the overall quality and competitiveness of the final product. The concept of digital twins is another cornerstone of this approach. By leveraging simulation data to create high-fidelity digital replicas of physical systems, engineers can perform predictive maintenance, optimize system performance, and conduct virtual testing. These digital twins facilitate rapid design iterations, minimize prototyping costs, and accelerate time-to-market. A collaborative environment further enhances this framework, fostering seamless knowledge sharing among engineers and scientists. Such platforms encourage interdisciplinary innovation, amplifying the impact of AI-driven insights across diverse domains. The integration of cloud-based HPC offers a flexible and cost-effective computing solution, enabling engineers to scale resources dynamically based on project demands. This ensures optimal utilization of computational power while maintaining cost-efficiency. Moreover, cloud-based storage provides scalable solutions for managing large simulation datasets, ensuring secure, centralized access for global teams. By adopting this data-centric strategy, industries such as automotive, high tech, life sciences, and manufacturing can unlock the full potential of AI to accelerate innovation, improve product performance, and reduce development costs. This framework exemplifies the synergistic possibilities at the intersection of AI, HPC, and engineering simulation, paving the way for a transformative future in digital engineering.