https://www.sciencedaily.com/releases/2018/07/180712123938.htm

In the past, machine learning was only capable of interpolating data -- making predictions about situations that are "between" other, known situations. It was incapable of extrapolating -- making predictions about situations outside of the known -- because it learns to fit the known data as closely as possible locally, regardless of how it performs outside of these situations. In addition, collecting sufficient data for effective interpolation is both time- and resource-intensive, and requires data from extreme or dangerous situations. But now, Georg Martius, former ISTFELLOW and IST Austria postdoc, and since 2017 a group leader at MPI for Intelligent Systems in Tübingen, Subham S. Sahoo, a PhD student also at MPI for Intelligent Systems, and Christoph Lampert, professor at IST Austria, developed a new machine learning method that addresses these problems, and is the first machine learning method to accurately extrapolate to unseen situations.

The key feature of the new method is that 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.