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

Abstract

The CAE field is facing enormous innovative opportunities offered by the rapid development of artificial intelligence (AI), particularly in text and image processing, as long as the unique challenges of complex CAE data are addressed. CAE engineers foresee AI as a potential tool not only for data management but also for enhancing complex engineering design processes across development stages. This presentation examines how AI could transform CAE with novel data representation and exploration techniques. We focus on AI-driven methods for visualizing and exploring model evolution and linking it to resulting effects, enabling inference and intelligent data retrieval. Implementing AI effectively in CAE requires transparent learning algorithms capable of handling limited but complex data, like mesh functions and geometries. Off-the-shelf AI models, such as large language models (LLMs), are not directly applicable. Therefore, we discuss emerging methods tailored to CAE challenges, highlighting AI's potential in engineering product development. First, we present the latest extensions of our Laplace-Beltrami shape feature approach (LBSFA) implemented in our software tool SimExplore to investigate similarities or exceptions in deformations and mesh functions, called events, in many simulations. The combined representation of model changes in geometry and input parameters, and results allows to set the model changes into context with the insights from simulation results analysis. This provides a fundamental basis for further post processing of results, like sensitivity and correlation analysis, up to envisioned learning of relationships. However, the engineer still needs to select mesh functions and components of interest out of a ranked list of components that are influenced by the model changes. To address this, we have investigated some more sophisticated filter and grouping methods to ease the decision on which component to look first. On the one hand, a method that filters out all parts without deformations but just influenced due to rigid body motion has proven to be very effective. On the other hand, concentrating on jumps in a development tree identifies the measures with the highest impact. In this sense, a jump means that the corresponding result jumps from one behavioral mode to another. Second, we explore how LLMs can be leveraged for CAE data. We present a proof of concept of using a Retrieval-Augmented Generation (RAG) method together with our structured data representation to enable easy exploration of relationships within the data of a development project. The RAG approach uses a knowledge base outside the training database and thus offers an extension or specialization in domain-specific knowledge. With this approach we could reach an automatic knowledge driven preselection of important components. Further on, fast metamodels can be generated based on this selection which are an efficient alternative for data-intensive convolutional neural networks (CNNs). Finally, we outline the different parts needed for an AI support system which learns measure-effect relationships. This contribution illustrates how far our AI driven solutions specialized for CAE applications can already reach to support the complex simulation data analysis tasks giving the engineers more resources for interpretation of results and decision making.