arXiv cs.LG
· Papers
Deep Neural Networks as Discrete Dynamical Systems: Implications for Physics-Informed Learning
arXiv:2601.00473v4 Announce Type: replace Abstract: We revisit the analogy between feed-forward deep neural networks (DNNs) and discrete dynamical systems derived from neural integral equations and their corresponding partial differential equation (PDE) forms. A comparative analysis between the numerical/exact solution