Physics and geometry model deep neural networks' feature learning

Recent research draws on physics and geometry to model feature learning in deep neural networks (DNNs), using analogies such as spring-block chains to explain how networks separate data layer by layer. This approach provides a new theoretical framework for understanding how DNNs generalize and perform across tasks. By relating network behavior to mechanical systems, the model offers insights into optimizing training, diagnosing overfitting, and improving generalization—potentially enabling more efficient development and evaluation of large-scale AI models.