But, what about accuracy? For GM’s purposes, Strauss says accuracy is not a huge concern at the design stage because finer details are ironed out later in the process. “When it really starts to matter is when we’re getting close to launching a vehicle, and the coefficient of drag is going to be used for our energy calculation, which eventually goes to the certification of our miles per gallon on the sticker.” At that stage, Strauss says, a physical model of the car will be put into a wind tunnel for an exact number.

All in all, by drastically bringing down the time it takes to model the physics, large physics models enable engineers to explore a much greater range of possibilities before a final design is reached.

So this is really just to help iterate on early designs without waiting 2 weeks each time to get feedback on if a design is problematic. This is actually a really great use of machine learning.

You gotta reach people where they are. Twitter doesn’t have as many eyeballs as the others.