It strives to reveal the true dynamics of the situation: it takes in data and returns the equations that describe the underlying physics. "If you know those equations," says Georg Martius, "then you can say what will happen in all situations, even if you haven't seen them." In other words, this is what allows the method to extrapolate reliably, making it unique among machine learning methods.
"In every other area of research, we expect models that make physical sense, that tell us why," adds Lampert. "This is what we should expect from machine learning, and what our method provides." Finally, in order to guarantee interpret-ability and optimize for physical situations, the team based their learning method on a different type of framework.