this post was submitted on 10 Apr 2026
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...Previously, a creative design engineer would develop a 3D model of a new car concept. This model would be sent to aerodynamics specialists, who would run physics simulations to determine the coefficient of drag of the proposed car—an important metric for energy efficiency of the vehicle. This simulation phase would take about two weeks, and the aerodynamics engineer would then report the drag coefficient back to the creative designer, possibly with suggested modifications.

Now, GM has trained an in-house large physics model on those simulation results. The AI takes in a 3D car model and outputs a coefficient of drag in a matter of minutes. “We have experts in the aerodynamics and the creative studio now who can sit together and iterate instantly to make decisions [about] our future products,” says Rene Strauss, director of virtual integration engineering at GM...

“What we’re seeing is that actually, these tools are empowering the engineers to be much more efficient,” Tschammer says. “Before, these engineers would spend a lot of time on low added value tasks, whereas now these manual tasks from the past can be automated using these AI models, and the engineers can focus on taking the design decisions at the end of the day. We still need engineers more than ever.”

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[–] XLE@piefed.social 4 points 7 hours ago (3 children)

Other people in this thread say physics simulations are inherently chaotic. If an AI model is trained on inherently chaotic data, how will the results not be chaotic or not worse?

[–] FauxLiving@lemmy.world 1 points 16 minutes ago

That is an insightful question.

The answer is that we actually understand chaos in a way. It isn't unpredictable in general, it's just hard to say how any given situation will evolve but we can understand a lot about how all systems will evolve.

I'm not good at explaining, but some content creators cover this topic pretty well. If you're interested, here's a video about it from Veritasium: https://www.youtube.com/watch?v=fDek6cYijxI

[–] Luminous5481@anarchist.nexus 6 points 4 hours ago

The universe is chaotic. But chaos doesn't mean something isn't reproducible or doesn't follow a set of rules.

[–] Scipitie@lemmy.dbzer0.com 19 points 7 hours ago

Because physically speaking, chaotic and unpredictable are two different things - and why it works so well on this case: it's becoming a stochastic problem, not a deterministic one.

It's an awesome area for machine learning: you didn't need to understand the result and how it got created, it just needs to be "close enough".