Deep learning is transforming most areas of science and technology, including electron
microscopy. This review paper offers a practical perspective aimed at developers with …

For over a century, psychology has focused on uncovering mental processes of a single
individual. However, humans rarely navigate the world in isolation. The most important …

The empirical performance of neural ordinary differential equations (NODEs) is significantly
inferior to discrete-layer models on benchmark tasks (eg image classification). We …

Microstructural evolution is a key aspect of understanding and exploiting the processing-
structure-property relationship of materials. Modeling microstructure evolution usually relies …

Artificial neural networks are universal function approximators. They can forecast dynamics,
but they may need impractically many neurons to do so, especially if the dynamics is chaotic …

The infinite-depth paradigm pioneered by Neural ODEs has launched a renaissance in the
search for novel dynamical system-inspired deep learning primitives; however, their …

We propose a novel training method that integrates rules into deep learning, in a way the
strengths of the rules are controllable at inference. Deep Neural Networks with Controllable …

The hierarchical model of galaxy formation predicts that the Milky Way halo is populated by
tidal debris of dwarf galaxies and globular clusters. Due to long dynamical times, debris from …

This is a collection of perspective pieces contributed by the participants of the Institute for
Nuclear Theory's Program on Nuclear Physics for Precision Nuclear Physics which was held …

We develop a class of emulators for solving quantum three-body scattering problems. They
are based on combining the variational method for scattering observables and the recently …