Can we build neural networks whose structure and computational abilities match a real brain? We are not quite there yet, but our new work shows a strategy for getting closer to this goal.

Synchrotron sources produce intense X-rays that are indispensable for many fields of modern science. The essential information, which is often buried in an ocean of experimental data, can be extracted using machine learning. But such models still lack important insights about the structure of the systems being studied. By allowing experimentalists to provide such insights to adaptive neural networks, we were able to instantly obtain more accurate results.

Diffusion processes in nature are highly complex, and scientists strive to understand them in detail. With a new physics-aware neural network, we were able to model and predict such processes much more precisely than previously possible.

Researchers train a neural network to estimate – in just a few seconds – the precise characteristics of merging black holes based on their gravitational-wave emissions. The network determines the masses and spins of the black holes, where in the sky, at what angle, and how far away from Earth the merger took place.