Centralized Data Management and AI: Driving Innovation in Engineering Simulation

John William (Rescale)

Abstract:

The integration of High-Performance Computing (HPC), Artificial Intelligence (AI), and Computer-Aided Engineering (CAE) is revolutionizing engineering simulation. While these technologies offer immense opportunities, managing vast simulation data remains a key challenge. A data-centric approach is crucial for streamlining workflows, optimizing resources, and enabling AI-driven insights across industries like automotive, aerospace, and manufacturing.

At the heart of this approach is centralized data management—a unified platform that stores, organizes, and shares simulation data from various sources. This centralization addresses fragmented workflows and siloed data management, which often lead to inefficiencies and delays. By creating a centralized repository, engineers can collaborate more effectively, sharing insights and accelerating product development.

AI plays a pivotal role by automating data analysis and improving simulation accuracy. AI algorithms, including machine learning and deep learning, process large datasets, identify patterns, and generate predictions. In industries such as automotive, AI-powered simulations predict vehicle performance and safety, reducing development time and enhancing product quality.

To maximize these technologies, adopting a digital thread is essential. This continuous, traceable flow of simulation data across the entire lifecycle enables collaboration and iterative optimization. AI-driven workflows within the digital thread ensure simulations are constantly updated, improving efficiency and accuracy.

Cloud-based HPC solutions offer scalable resources, reducing the need for large upfront investments in infrastructure. These solutions allow companies to perform large-scale simulations cost-effectively without compromising on resource utilization.

Implementing AI-enhanced methodologies and centralized data management helps engineering teams overcome traditional CAE workflow barriers. Case studies from automotive, aerospace, and manufacturing industries show tangible benefits, such as reduced simulation runtimes, improved throughput, and faster decision-making. These advancements help organizations address complex design challenges and bring high-quality products to market faster.

The presentation will explore how modernizing CAE workflows with centralized data management, AI, and the digital thread can drive the next wave of innovation in engineering simulation.