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

Abstract

Today's engineering systems, whether in aerospace, automotive, or in other domains, are becoming increasingly complex. Manually setting up and running (multiphysics) 3D-simulations for these complex systems is time-consuming and error-prone. At the same time, the trend towards faster innovation cycles and the need for improved product performance require more excessive design space exploration. As the number of design iterations and thus the number of simulations increase, the effort and expertise required to setup complex simulation becomes a major limiting factor. And this trend will become even more aggravated due to the relative shortage of CAE experts, which prevents leveraging ever increasing available compute power. While in the recent years many investments have been targeted at accelerating solver processing, pre-and post-processing workflows have remained mostly unchanged. Increasing the level of autonomation in CAE workflows offers therefore a major opportunity. The latest advancements in Generative AI have unlocked unprecedented possibilities of automation through autonomous AI Agents. AI agents are autonomous software systems that can perceive their environment, make decisions, and take actions to achieve specific goals. Within this presentation, we demonstrate along two specific examples how such autonomous agents can be tailored to independently perform various tasks associated with the modelling, pre-and post-processing of simulation, and analysis of complex engineering systems. The first example addresses how autonomous AI agents can realize the vision of fully autonomous design validation within Computer Aided Design workflows. We demonstrate such an autonomous design validation workflow on a 3D structural simulation of a jet engine bracket. Starting from the specification given to the designer, the copilot autonomously infers loads, boundary conditions, and material properties from the geometry of the model and any accompanying textual specifications, and executes the simulation. Once the simulation is complete, the results are summarized in a report, providing non-expert users with sufficient information to determine whether the design is viable. The second example demonstrates how Generative AI, specifically their vision capabilities, can be used for CAD part recognition and classification. This in turn is a prerequisite for automated modelling defeaturing and abstractions. Preprocessing times are then substantially reduced, especially when considering massive CAD assemblies. Furthermore, we outline how Knowledge Graph based Simulation Data Management allows to reduce hallucinations of the CAE Agents. The concepts will be demonstrated within the Simcenter Tools along the two concrete examples but allow for a generalization also to other contexts. This approach tremendously reduces the complexity and effort of CAE workflows ultimately allowing non-expert users to perform virtual design validation.