Artificial intelligence models are jacks of many trades, including writing, generating images, and creating 3D models. But they aren’t as helpful when it comes to testing robots or designs for vehicles in diverse environments, since they don’t understand physics as well as they do pixels or text.
To build an AI system that can reliably simulate a variety of physical scenarios, engineers need physics data at a scale that is not yet feasible. That’s because it is very time-consuming to generate even a few data points that neural networks can understand. They rely on algorithms called “numerical solvers” to calculate physical properties at different points of a 3D shape...
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