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Live+7 todayUpdated 1h ago

BMW Group and Mistral AI partner to use AI in crash simulation

The BMW Group and Mistral AI are collaborating to apply artificial intelligence to crash simulation, aiming to improve quality, accuracy, and speed in vehicle development.

Crash simulation data volume

over one petabyte

What Happened

The BMW Group and Mistral AI have announced a partnership to advance the use of AI in crash simulation. The collaboration focuses on training AI models with industrial datasets to improve complex engineering tasks. Over time, BMW has accumulated over one petabyte of crash simulation data, providing a unique foundation for developing specialized AI.

For the BMW Group, the use of industrial data is a key factor in translating artificial intelligence into value creation. By combining our engineering datasets with Mistral AI's model training capabilities, we are building specialized AI which supports complex development tasks.
Dr. Franz Decker, CIO and Senior Vice President of the BMW Group
As Industrial AI becomes the new frontier for AI, we are proud to partner with the BMW Group. This collaboration shows how industry specific AI models can help solve complex engineering challenges such as crash simulation.
Marjorie Janiewicz, Chief Revenue Officer of Mistral AI

The BMW Group is using Large Industry Models (LIM) – AI systems trained on industry-specific data – to embed domain knowledge directly into the AI. This approach requires deep expertise and environments that allow AI to learn from BMW's development processes.

Why this matters

This partnership could accelerate development of safer vehicles by using AI to analyze vast amounts of crash simulation data more efficiently than traditional methods.

Terms in This Story

Large Industry Model (LIM)
An AI system trained on industry-specific data to embed domain knowledge, unlike general-purpose AI.
crash simulation
A computer-based simulation of vehicle crashes to analyze safety and structural behavior.
Read Original: BMW

Summarised from the linked release; details can be imperfect — always verify against the original source.