https://phys.org/news/2018-07-machine-method-capable-accurate-extrapolation.html

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.

The team's method sets itself apart in several other ways as well. First, the final approximations previously produced during machine learning were far too complex for a human to understand or work with. In the new method, the resulting equations are far simpler: "Our method's equations are something you would see in a textbook—simple and intuitive," says Christoph Lampert. The latter is another key difference: other machine learning methods give no insight into the relationship between conditions and results—and thus, no intuition on whether the model is even plausible. "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 interpretability and optimize for physical situations, the team based their learning method on a different type of framework. This new design is simpler than previous methods, which in practice means that less data is needed to give the same or even better results.