Hamiltonian Neural Networks from a Differential Geometry Perspective

A blog post explores Hamiltonian Neural Networks (HNNs) through a differential-geometry lens, focusing on symplectic structure rather than the conventional loss-function formulation. The article targets readers with geometry background and aims to deepen understanding of HNNs' underlying mechanics.