BMW Group and Mistral AI advance AI in crash simulation – AutoMart Canada

“For the BMW Group, the use of industrial data is a key factor in
translating artificial intelligence into value creation,” said Dr.
Franz Decker, CIO and Senior Vice President of the BMW Group. “By
combining our engineering datasets with Mistral AI’s model training
capabilities, we are building specialized AI which supports complex
development tasks.”  

Complexity and Data Volume in Crash Simulation  

The scale and complexity of crash simulation at the BMW Group
underline the need for domain-specific AI. Each week, the company
runs thousands of virtual crash simulations, generating vast amounts
of engineering data. Over time, this has resulted in a historical
dataset of over one petabyte of crash simulation data. It provides
highly detailed insights into vehicle structures and material
behaviour, forming a unique foundation for training an industrial AI
model.  

“As Industrial AI becomes the new frontier for AI, we are proud to
partner with the BMW Group” said Marjorie Janiewicz, Chief Revenue
Officer of Mistral AI. “This collaboration shows how industry
specific AI models can help solve complex engineering challenges
such as crash simulation.”  

Large Industry Model as technical foundation  

To scale this approach, the BMW Group is focusing on so-called
Large Industry Models (LIM). These are AI systems trained on
industry specific engineering and simulation data from vehicle
development and safety testing. Unlike general‑purpose AI systems,
LIMs embed domain‑specific knowledge directly into the AI model.
This requires not only industrial data, but also deep domain
expertise and technical environments that allow AI systems to learn
directly from BMW’s development processes.  

The partnership highlights the importance of industrial data for
the next phase of data‑driven value creation and strengthens the BMW
Group’s AI and innovation ecosystem.