Master Complexity of Version Management in SDM by AI-Driven Assistance

Simulation-driven product development involves numerous CAE model iterations, where each version represents a critical difference. Usually, these multiple model versions are generated by hundreds of simulation engineers working in teams distributed across the globe, making functional collaboration a key for effective product development. To manage vast amounts of CAE data generated by engineers working simultaneously on a project, it is imperative to have a robust version management system to track changes in the CAE data. A robust version management is the backbone of an effective simulation data management (SDM) system. It involves capturing and documenting model changes at every design iteration. An accurate documentation of the model changes is crucial as it helps in understanding the model evolution and collaboration among engineers. However, documenting is usually considered a boring and tedious task by many engineers. This often leads to bad change documentation, which in turn reduces the data discoverability and causes knowledge loss. With the onset of AI in engineering simulations, engineers can now learn even more from their simulation data. In this paper, authors have explored an AI-assisted approach for facilitating the change documentation by augmenting the change comments via automatically extracted details, as studied in the SAFECAR-ML research project. A goal of SAFECAR-ML is to develop an AI model that understands the nature of design changes and thereupon automatically generates change descriptions. When a detailed and informative change documentation is available, LLM-based generative AI can be used for discovering and creating simulation-related content in an SDM system, for example by using RAG approaches. A long-term outlook is to build an AI-assisted capability to perform complex tasks in an SDM system, like search and summarisation of the data, automatic evaluation of simulation results, and research on thinking models for making recommendations on further model changes.