ML

Mechanistic Interpretability promises a path toward fully understanding how neural networks make their predictions. We argue that neural networks can learn low-dimensional representations of their training data that are surprisingly faithful to human-derived domain knowledge. As a case study, we extract nuclear physics concepts by studying models trained to reproduce nuclear data.

We introduce Nuclear Co-Learned Representations (NuCLR), a deep learning model that predicts various nuclear observables, including binding and decay energies, and nuclear charge radii. The model is trained using a multi-task approach with shared representations and obtains state-of-the-art performance, achieving levels of precision that are crucial for understanding fundamental phenomena in nuclear (astro)physics.